Data mining Books
O'Reilly Media Just Hibernate
Book SynopsisIf you're looking for a short, sweet, and simple introduction (or reintroduction) to Hibernate, this is the book you want. Through clear real-world examples, you'll learn Hibernate and object-relational mapping from the ground up, starting with the basics. Then you'll dive into the framework's moving parts to understand how they work in action.
£19.19
O'Reilly Media Data Mining
Book SynopsisThis non-technical guide shows you how to extract significant business value from big data with Ask-Measure-Learn, a system that helps you ask the right questions, measure the right data, and then learn from the results.
£19.19
O'Reilly Media eXist
Book SynopsisWith this hands-on guide, you'll learn eXist from the ground up, from using this feature-rich database to work with millions of documents to building complex web applications that take advantage of eXist's many extensions.
£28.79
O'Reilly Media HBase
Book SynopsisIf your organization is looking for a storage solution to accommodate a virtually endless amount of data, this book will show you how Apache HBase can fulfill your needs.
£25.59
Springer-Verlag New York Inc. Encyclopedia of Database Systems
Book Synopsis.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor TexTable of Contents.NET Remoting.- Absolute Time.- Abstract Versus Concrete Temporal Query Languages.- Abstraction.- Access Control.- Access Control Administration Policies.- Access Control Policy Languages.- Access Path.- ACID Properties.- Active and Real-Time Data Warehousing.- Active Database Coupling Modes.- Active Database Execution Model.- Active Database Knowledge Model.- Active Database Management System Architecture.- Active Database Rulebase.- Active Database, Active Database (Management) System.- Active Storage.- Active XML.- Activity.- Activity Diagrams.- Actors/Agents/Roles.- Adaptive Interfaces.- Adaptive Middleware for Message Queuing Systems.- Adaptive Query Processing.- Adaptive Stream Processing.- ADBMS.- Administration Model for RBAC.- Administration Wizards.- Advanced Information Retrieval Measures.- Aggregation: Expressiveness and Containment.- Aggregation-Based Structured Text Retrieval.- Air Indexes for Spatial Databases.- AJAX.- Allen's Relations.- AMOSQL.- AMS Sketch.- Anchor Text.- Annotation.- Annotation-based Image Retrieval.- Anomaly Detection on Streams.- Anonymity.- ANSI/INCITS RBAC Standard.- Answering Queries Using Views.- Anti-monotone Constraints.- Applicability Period.- Application Benchmark.- Application Recovery.- Application Server.- Application-Level Tuning.- Applications of Emerging Patterns for Microarray Gene Expression Data Analysis.- Applications of Sensor Network Data Management.- Approximate Queries in Peer-to-Peer Systems.- Approximate Query Processing.- Approximate Reasoning.- Approximation of Frequent Itemsets.- Apriori Property and Breadth-First Search Algorithms.- Architecture-Conscious Database System.- Archiving Experimental Data.- Armstrong Axioms.- Array Databases.- Array Databases_old.- Association Rule Mining on Streams.- Association Rules.- Asymmetric Encryption.- Atelic Data.- Atomic Event.- Atomicity.- Audio.- Audio Classification.- Audio Content Analysis.- Audio Metadata.- Audio Representation.- Audio Segmentation.- Auditing and Forensic Analysis.- Authentication.- Automatic Image Annotation.- Autonomous Replication.- Average Precision.- Average Precision at n.- Average Precision Histogram.- Average R-Precision.- B+-Tree.- Backup and Restore.- Bag Semantics.- Bagging.- Bayesian Classification.- Benchmark Frameworks.- Benchmarks for Big Data Analytics.- Big Data Platforms for Data Analytics.- Big Stream Systems.- Biological Metadata Management.- Biological Networks.- Biological Resource Discovery.- Biological Sequences.- Biomedical Data/Content Acquisition, Curation.- Biomedical Image Data Types and Processing.- Biomedical Scientific Textual Data Types and Processing.- Biostatistics and Data Analysis.- Bi-Temporal Indexing.- Bitemporal Interval.- Bitemporal Relation.- Bitmap Index.- Bitmap-based Index Structures.- Blind Signatures.- Bloom Filters.- BM25.- Boolean Model.- Boosting.- Bootstrap.- Boyce-Codd Normal Form.- BP-Completeness.- Bpref.- Browsing.- Browsing in Digital Libraries.- B-Tree Locking.- Buffer Management.- Buffer Manager.- Buffer Pool.- Business Intelligence.- Business Process Execution Language.- Business Process Management.- Business Process Modeling Notation.- Business Process Reengineering.- Cache-Conscious Query Processing.- Calendar.- Calendric System.- CAP Theorem.- Cardinal Direction Relationships.- Cartesian Product.- Cataloging in Digital Libraries.- Causal Consistency.- Certain (and Possible) Answers.- Change Detection on Streams.- Channel-Based Publish/Subscribe.- Chart.- Chase.- Checksum and Cyclic Redundancy Check Mechanism.- Choreography.- Chronon.- Citation.- Classification.- Classification by Association Rule Analysis.- Classification in Streams.- Client-Server Architecture.- Clinical Data Acquisition, Storage and Management.- Clinical Data and Information Models.- Clinical Data Quality and Validation.- Clinical Decision Support.- Clinical Document Architecture.- Clinical Event.- Clinical Knowledge Repository.- Clinical Observation.- Clinical Ontologies.- Clinical Order.- Closed Itemset Mining and Non-redundant Association Rule Mining.- Closest-Pair Query.- Cloud Computing.- Cloud Intelligence.- Cluster and Distance Measure.- Clustering for Post Hoc Information Retrieval.- Clustering on Streams.- Clustering Overview and Applications.- Clustering Validity.- Clustering with Constraints.- Collaborative Filtering.- Column Segmentation.- Column Stores.- Common Warehouse Metamodel.- Comparative Visualization.- Compensating Transactions.- Complex Event.- Complex Event Processing.- Composed Services and WS-BPEL.- Composite Event.- Composition.- Comprehensions.- Compression of Mobile Location Data.- Computational Media Aesthetics.- Computationally Complete Relational Query Languages.- Computerized Physician Order Entry.- Conceptual Modeling Foundations.- Conceptual Schema Design.- Concurrency Control - Traditional Approaches.- Concurrency Control for Replicated Databases.- Concurrency Control Manager.- Conditional Tables.- Conjunctive Query.- Connection.- Consistency Models For Replicated Data.- Consistent Query Answering.- Constraint Databases.- Constraint Query Languages.- Constraint-Driven Database Repair.- Content-and-Structure Query.- Content-Based Publish/Subscribe.- Content-Based Video Retrieval.- Content-Only Query.- Context.- Contextualization in Structured Text Retrieval.- Continuous Data Protection.- Continuous Monitoring of Spatial Queries.- Continuous Multimedia Data Retrieval.- Continuous Queries in Sensor Networks.- Continuous Query.- ConTract.- Control Data.- Convertible Constraints.- Coordination.- Copyright Issues in Databases.- CORBA.- Correctness Criteria Beyond Serializability.- Cost and quality trade-offs in crowdsourcing.- Cost Estimation.- Count-Min Sketch.- Coupling and De-coupling.- Covering Index.- Crash Recovery.- Cross-Language Mining and Retrieval.- Cross-Modal Multimedia Information Retrieval.- Cross-Validation.- Crowd Database Operators.- Crowd Database Systems.- Crowd Mining and Analysis.- Crowdsourcing Geographic Information Systems.- Cube.- Cube Implementations.- Current Semantics.- Curse of Dimensionality.- Daplex.- Data Acquisition and Dissemination in Sensor Networks.- Data Aggregation in Sensor Networks.- Data Broadcasting, Caching and Replication in Mobile Computing.- Data Cleaning.- Data Compression in Sensor Networks.- Data Conflicts.- Data Definition.- Data Definition Language (DDL).- Data Dictionary.- Data Encryption.- Data Estimation in Sensor Networks.- Data Exchange.- Data Fusion.- Data Fusion in Sensor Networks.- Data Generation.- Data Governance.- Data Integration Architectures and Methodology for the Life Sciences.- Data Integration in Web Data Extraction System.- Data Management for VANETs.- Data Management Fundamentals: Database Management System.- Data Management in Data Centers.- Data Manipulation.- Data Manipulation Language (DML).- Data Mart.- Data Migration Management.- Data Mining.- Data Partitioning.- Data Privacy and Patient Consent.- Data Profiling.- Data Provenance.- Data Quality Assessment.- Data Quality Dimensions.- Data Quality Models.- Data Rank/Swapping.- Data Reduction.- Data Replication.- Data Sampling.- Data Scrubbing.- Data Sketch/Synopsis.- Data Skew.- Data Storage and Indexing in Sensor Networks.- Data Stream.- Data Stream Management Architectures and Prototypes.- Data Types in Scientific Data Management.- Data Uncertainty Management in Sensor Networks.- Data Visualization.- Data Warehouse.- Data Warehouse Life-Cycle and Design.- Data Warehouse Maintenance, Evolution and Versioning.- Data Warehouse Metadata.- Data Warehouse Security.- Data Warehousing for Clinical Research.- Data Warehousing in Cloud Environments.- Data Warehousing on Non-Conventional Data.- Data Warehousing Systems: Foundations and Architectures.- Data, Text, and Web Mining in Healthcare.- Database.- Database Adapter and Connector.- Database Administrator (DBA).- Database Appliances.- Database Benchmarks.- Database Clustering Methods.- Database Clusters.- Database Dependencies.- Database Design.- Database Languages for Sensor Networks.- Database Machine.- Database Management System.- Database Middleware.- Database Repair.- Database Reverse Engineering.- Database Schema.- Database Security.- Database System.- Database Techniques to Improve Scientific Simulations.- Database Trigger.- Database Tuning using Combinatorial Search.- Database Tuning using Online Algorithms.- Database Tuning using Trade-off Elimination.- Database Use in Science Applications.- Datalog.- DBMS Component.- DBMS Interface.- DCE.- DCOM.- Decay Models.- Decision Rule Mining in Rough Set Theory.- Decision Tree Classification.- Decision Trees.- Declarative Networking.- Deductive Data Mining using Granular Computing.- Deduplication.- Deduplication in Data Cleaning.- Deep Instantiation.- Deep-Web Search.- Dense Index.- Dense Pixel Displays.- Density-based Clustering.- Description Logics.- Design for Data Quality.- Dewey Decimal System.- Diagram.- Difference.- Differential Privacy.- Digital Archives and Preservation.- Digital Curation.- Digital Elevation Models.- Digital Libraries.- Digital Rights Management.- Digital Signatures.- Dimension.- Dimension Reduction Techniques for Clustering.- Dimensionality Reduction.- Dimensionality Reduction Techniques For Nearest Neighbor Computations.- Dimension-Extended Topological Relationships.- Direct Attached Storage.- Direct Manipulation.- Disaster Recovery.- Disclosure Risk.- Discounted Cumulated Gain.- Discovery.- Discrete Wavelet Transform and Wavelet Synopses.- Discretionary Access Control.- Disk.- Disk Power Saving.- Distortion Techniques.- Distributed Architecture.- Distributed Concurrency Control.- Distributed Data Streams.- Distributed Database Design.- Distributed Database Systems.- Distributed DBMS.- Distributed Deadlock Management.- Distributed File Systems.- Distributed Hash Table.- Distributed Join.- Distributed Machine Learning.- Distributed Query Optimization.- Distributed Query Processing.- Distributed Recovery.- Distributed Spatial Databases.- Distributed Transaction Management.- Divergence from Randomness Models.- D-measure.- Document.- Document Clustering.- Document Databases.- Document Field.- Document Length Normalization.- Document Links and Hyperlinks.- Document Representations (Inclusive Native and Relational).- Dublin Core.- Dynamic Graphics.- Dynamic Web Pages.- eAccessibility.- ECA Rule Action.- ECA Rule Condition.- ECA Rules.- e-Commerce Transactions.- Effectiveness Involving Multiple Queries.- Ehrenfeucht-Fraïssé Games.- Elasticity.- Electronic Dictionary.- Electronic Encyclopedia.- Electronic Health Record.- Electronic Ink Indexing.- Electronic Newspapers.- Eleven Point Precision-recall Curve.- Emergent Semantics.- Emerging Pattern Based Classification.- Emerging Patterns.- Energy Efficiency in Data Centers.- Ensemble.- Enterprise Application Integration.- Enterprise Content Management.- Enterprise Service Bus.- Enterprise Terminology Services.- Entity Relationship Model.- Entity Resolution.- Entity Retrieval.- Equality-Generating Dependencies.- ERR- Expected Reciprocal Rank.- ERR-IA Intent-aware ERR.- Escrow Transactions.- European Law in Databases.- Evaluation Metrics for Structured Text Retrieval.- Evaluation of Relational Operators.- Event.- Event and Pattern Detection over Streams.- Event Causality.- Event Channel.- Event Cloud.- Event Detection.- Event Driven Architecture.- Event Flow.- Event in Active Databases.- Event in Temporal Databases.- Event Lineage.- Event Pattern Detection.- Event Prediction.- Event Processing Agent.- Event Processing Network.- Event Sink.- Event Source.- Event Specification.- Event Stream.- Event Transformation.- Event-Driven Business Process Management.- Eventual Consistency.- Evidence Based Medicine.- Executable Knowledge.- Execution Skew.- Explicit Event.- Exploratory Data Analysis.- Expressive Power of Query Languages.- Extended Entity-Relationship Model.- Extended Transaction Models and the ACTA Framework.- Extendible Hashing.- Extraction, Transformation, and Loading.- Faceted Search.- Fault-Tolerance and High Availability in Data Stream Management Systems.- Feature Extraction for Content-Based Image Retrieval.- Feature Selection for Clustering.- Feature-Based 3D Object Retrieval.- Field-Based Information Retrieval Models.- Field-Based Spatial Modeling.- First-Order Logic: Semantics.- First-Order Logic: Syntax.- Fixed Time Span.- Flex Transactions.- FM Synopsis.- F-Measure.- Focused Web Crawling.- FOL Modeling of Integrity Constraints (Dependencies).- Forever.- Form.- Fourth Normal Form.- FQL.- Fractal.- Frequency Moments.- Frequent Graph Patterns.- Frequent Items on Streams.- Frequent Itemset Mining with Constraints.- Frequent Itemsets and Association Rules.- Frequent Partial Orders.- Fully-Automatic Web Data Extraction.- Functional Data Model.- Functional Dependencies for Semi-Structured Data.- Functional Dependency.- Functional Query Language.- Fuzzy Models.- Fuzzy Relation.- Fuzzy Set.- Fuzzy Set Approach.- Fuzzy/Linguistic IF-THEN Rules and Linguistic Descriptions.- Gazetteers.- Gene Expression Arrays.- Generalization of ACID Properties.- Generalized Search Tree.- Genetic Algorithms.- Geographic Information System.- Geographical Information Retrieval.- Geography Markup Language.- Geometric Stream Mining.- GEO-RBAC Model.- Georeferencing.- Geosocial Networks.- Geospatial Metadata.- Geo-Targeted Web Search.- GMAP.- Grammar Inference.- Graph.- Graph Data Management in Scientific Applications.- Graph Database.- Graph Management in the Life Sciences.- Graph Mining.- Graph Mining on Streams.- Graph OLAP.- Graphical Models for Uncertain Data Management.- Grid and Workflows.- Grid File (and Family).- GUIs for Web Data Extraction.- Hash Functions.- Hash Join.- Hash-based Indexing.- Healthcare Metrics.- Hierarchial Clustering.- Hierarchical Data Model.- Hierarchical Data Summarization.- Hierarchical Heavy Hitter Mining on Streams.- Hierarchy.- High Dimensional Indexing.- Histogram.- Histograms on Streams.- History in Temporal Databases.- Homomorphic Encryption.- Horizontally Partitioned Data.- Human Factors Modeling in Crowdsourcing.- Human-centered Computing: Application to Multimedia.- Human-Computer Interaction.- Hypertexts.- I/O Model of Computation.- Icon.- Iconic Displays.- Image.- Image Content Modeling.- Image Database.- Image Management for Biological Data.- Image Metadata.- Image Querying.- Image Representation.- Image Retrieval and Relevance Feedback.- Image Segmentation.- Image Similarity.- Implementation of Database Operators (Joins, Group by, etc.).- Implication of Constraints.- Implications of Genomics for Clinical Informatics.- Implicit Event.- Incomplete Information.- Inconsistent Databases.- Incremental Computation of Queries.- Incremental Crawling.- Incremental Maintenance of Views with Aggregates.- Index Creation and File Structures.- Index Join.- Index Structures for Biological Sequences.- Index Tuning.- Indexed Sequential Access Method.- Indexing and Similarity Search.- Indexing Compressed Text.- Indexing Historical Spatio-Temporal Data.- Indexing in pub/sub systems.- Indexing Metric Spaces.- Indexing of Data Warehouses.- Indexing of the Current and Near-Future Positions of Moving Objects.- Indexing Techniques for Multimedia Data Retrieval.- Indexing the Web.- Indexing Uncertain Data.- Indexing Units of Structured Text Retrieval.- Indexing with Crowds.- Individually Identifiable Data.- Inference Control in Statistical Databases.- Information Extraction.- Information Filtering.- Information Foraging.- Information Integration.- Information Integration Techniques for Scientific Data.- Information Lifecycle Management.- Information Loss Measures.- Information Navigation.- Information Quality.- Information Quality and Decision Making.- Information Quality Assessment.- Information Quality Policy and Strategy.- Information Quality: Managing Information as a Product.- Information Retrieval.- Information Retrieval Models.- Information Retrieval Operations.- Infrastructure As-A-Service (IaaS).- Initiative for the Evaluation of XML Retrieval.- Initiator.- In-Network Query Processing.- Integrated DB and IR Approaches.- Integration of Rules and Ontologies.- Intelligent Storage Systems.- Interactive Analytics in Social Media.- Interface.- Interface Engines in Healthcare.- Interoperability in Data Warehouses.- Interoperation of NLP-based Systems with Clinical Databases.- Inter-Operator Parallelism.- Inter-Query Parallelism.- Intra-operator Parallelism.- Intra-Query Parallelism.- Intrusion Detection Technology.- Inverse Document Frequency.- Inverted Files.- IP Storage.- Iterator.- Java Database Connectivity.- Java Enterprise Edition.- Java Metadata Facility.- Join.- Join Dependency.- Join Index.- Join Order.- k-Anonymity.- Karp-Luby Sampling.- KDD Pipeline.- Key.- K-Means and K-Medoids.- Knowledge Base.- Knowledge Base Extraction.- Language Models.- Languages for Web Data Extraction.- Learning Distance Measures.- Lexical Analysis of Textual Data.- Licensing and Contracting Issues in Databases.- Lifespan.- Lightweight Ontologies.- Linear Hashing.- Linear Regression.- Linked Open Data.- Linking and Brushing.- Load Balancing in Peer-to-Peer Overlay Networks.- Load Shedding.- LOC METS.- Locality.- Locality of Queries.- Location Based Recommendation.- Location Management in Mobile Environments.- Location Update Management.- Location-Based Services.- Locking Granularity and Lock Types.- Logging and Recovery.- Logging/Recovery Subsystem.- Logical and Physical Data Independence.- Logical Database Design: from Conceptual to Logical Schema.- Logical Document Structure.- Logical Foundations of Web Data Extraction.- Logical Models of Information Retrieval.- Logical Unit Number.- Logical Unit Number Mapping.- Logical Volume Manager.- Log-Linear Regression.- Loop.- Loose Coupling.- Machine Learning in Computational Biology.- Main Memory.- Main Memory DBMS.- Maintenance of Materialized Views with Outer-Joins.- Maintenance of Recursive Views.- Managing Compressed Structured Text.- Managing Data Integration Uncertainty.- Managing Probabilistic Entity Extraction.- Mandatory Access Control.- MANET Databases.- MAP.- Map Matching.- MapReduce.- Markup Language.- MashUp.- Massive Array of Idle Disks.- Matrix Masking.- Max-Pattern Mining.- Mean Reciprocal Rank.- Measure.- Mediation.- Membership Query.- Memory Hierarchy.- Memory Locality.- Merkle Trees.- Message Authentication Codes.- Message Queuing Systems.- Meta Data Repository.- Meta Object Facility.- Metadata.- Metadata Interchange Specification.- Metadata Registry, ISO/IEC 11179.- Metamodel.- Metasearch Engines.- Metric Space.- Microaggregation.- Microbenchmark.- Microdata.- Microdata Rounding.- Middleware Support for Database Replication and Caching.- Middleware Support for Precise Failure Semantics.- Mining of Chemical Data.- Mobile Database.- Mobile Interfaces.- Mobile resource search.- Mobile Sensor Network Data Management.- Model Management.- Model-based Querying in Sensor Networks.- Monotone Constraints.- Monte Carlo Methods for Uncertain Data.- Moving Object.- Moving Objects Databases and Tracking.- MRR.- Multi-Data Center Consistency Properties.- Multi-Data Center Replication Protocols.- Multidimensional Data Formats.- Multidimensional Modeling.- Multidimensional Scaling.- Multi-Level Modeling.- Multi-Level Recovery and the ARIES Algorithm.- Multilevel Secure Database Management System.- Multilevel Transactions and Object-Model Transactions.- Multimedia Data.- Multimedia Data Buffering.- Multimedia Data Indexing.- Multimedia Data Querying.- Multimedia Data Storage.- Multimedia Databases.- Multimedia Information Retrieval Model.- Multimedia Metadata.- Multimedia Presentation Databases.- Multimedia Resource Scheduling.- Multimedia Retrieval Evaluation.- Multimedia Tagging.- Multimodal Interfaces.- Multi-Pathing.- Multiple Representation Modeling.- Multi-Query Optimization.- Multi-Resolution Terrain Modeling.- Multi-Step Query Processing.- Multitenancy.- Multi-Tier Architecture.- Multi-tier Storage Systems.- Multivalued Dependency.- Multivariate Visualization Methods.- Multi-version Serializability and Concurrency Control.- Naive Tables.- Narrowed Extended XPath I.- Natural Interaction.- Near-duplicate Retrieval.- Nearest Neighbor Classification.- Nearest Neighbor Query.- Nearest Neighbor Query in Spatio-temporal Databases.- Nested Loop Join.- Nested Transaction Models.- Network Attached Secure Device.- Network Attached Storage.- Network Data Model.- Neural Networks.- N-Gram Models.- Noise Addition.- Nonparametric Data Reduction Techniques.- Non-Perturbative Masking Methods.- Non-relational Streams.- Nonsequenced Semantics.- Normal Form ORA-SS Schema Diagrams.- Normal Forms and Normalization.- NoSQL Stores.- Now in Temporal Databases.- Null Values.- OASIS.- Object Constraint Language.- Object Data Models.- Object Identity.- Object Recognition.- Object Relationship Attribute Data Model for Semi-structured Data.- Object Storage Protocol.- Object-Role Modeling.- OLAM.- OLAP Personalization and Recommendation.- OLAP Personalization and Recommendation_old.- One-Copy-Serializability.- One-Pass Algorithm.- On-Line Analytical Processing.- Online Recovery in Parallel Database Systems.- Ontologies and Life Science Data Management.- Ontology.- Ontology Elicitation.- Ontology Engineering.- Ontology Visual Querying.- Ontology-Based Data Access and Integration.- Open Database Connectivity.- Open Information Extraction.- Open Nested Transaction Models.- Operator-Level Parallelism.- Opinion Mining.- Optimistic Replication and Resolution.- Optimization and Tuning in Data Warehouses.- OQL.- Orchestration.- Order Dependency.- OR-Join.- OR-Split.- OSQL.- Outlier Detection.- Overlay Network.- OWL: Web Ontology Language.- P/FDM.- Parallel and Distributed Data Warehouses.- Parallel Coordinates.- Parallel Data Placement.- Parallel Database Management.- Parallel Hash Join, Parallel Merge Join, Parallel Nested Loops Join.- Parallel Query Execution Algorithms.- Parallel Query Optimization.- Parallel Query Processing.- Parameterized Complexity of Queries.- Parametric Data Reduction Techniques.- Partial Replication.- Path Query.- Pattern-Growth Methods.- Peer Data Management System.- Peer to Peer Overlay Networks: Structure, Routing and Maintenance.- Peer-To-Peer Content Distribution.- Peer-to-Peer Data Integration.- Peer-to-Peer Publish-Subscribe Systems.- Peer-to-Peer Storage.- Peer-to-Peer System.- Peer-to-Peer Web Search.- Performance Analysis of Transaction Processing Systems.- Performance Monitoring Tools.- Period-Stamped Temporal Models.- Personalized Web Search.- Petri Nets.- Physical Clock.- Physical Database Design for Relational Databases.- Physical Layer Tuning.- Pipeline.- Pipelining.- Platform As-A-Service (PaaS).- Point-in-Time Copy.- Point-Stamped Temporal Models.- Polytransactions.- Positive Relational Algebra.- Possible Answers.- PRAM.- Precision.- Precision and Recall.- Precision at n.- Precision-Oriented Effectiveness Measures.- Predictive Analytics.- Preference Queries.- Preference Specification.- Prescriptive Analytics.- Presenting Structured Text Retrieval Results.- Primary Index.- Principal Component Analysis.- Privacy.- Privacy Metrics.- Privacy Policies and Preferences.- Privacy through Accountability.- Privacy-Enhancing Technologies.- Privacy-Preserving Data Mining.- Privacy-Preserving DBMSs.- Private Information Retrieval.- Probabilistic Databases.- Probabilistic Entity Resolution.- Probabilistic Retrieval Models and Binary Independence Retrieval (BIR) Model.- Probabilistic Skylines.- Probabilistic Spatial Queries.- Probabilistic Temporal Databases.- Probability Ranking Principle.- Probability Smoothing.- Process Life Cycle.- Process Mining.- Process Modeling.- Process Optimization.- Process Structure of a DBMS.- Processing Overlaps in Structured Text Retrieval.- Processing Structural Constraints.- Processor Cache.- Profiles and Context for Structured Text Retrieval.- Projection.- Propagation-based Structured Text Retrieval.- Protection from Insider Threats.- Provenance.- Provenance and Reproducibility.- Provenance in Databases.- Provenance in Scientific Databases.- Provenance in Workflows.- Provenance Management.- Provenance Standards.- Provenance Storage.- Provenance: Privacy and Security.- Pseudonymity.- Publish/Subscribe.- Publish/Subscribe over Streams.- Punctuations.- Q-measure.- Quadtrees (and Family).- Qualitative Temporal Reasoning.- Quality and Trust of Information Content and Credentialing.- Quality of Data Warehouses.- Quantiles on Streams.- Quantitative Association Rules.- QUEL.- Query by Humming.- Query Containment.- Query Evaluation Techniques for Multidimensional Data.- Query Expansion for Information Retrieval.- Query Expansion Models.- Query Language.- Query Languages and Evaluation Techniques for Biological Sequence Data.- Query Languages for the Life Sciences.- Query Load Balancing in Parallel Database Systems.- Query Optimization.- Query Optimization (in Relational Databases).- Query Optimization in Sensor Networks.- Query Plan.- Query Point Movement Techniques for Content-Based Image Retrieval.- Query Processing.- Query Processing (in Relational Databases).- Query Processing and Optimization in Object Relational Databases.- Query Processing in data integration systems.- Query Processing in Data Warehouses.- Query Processing in Deductive Databases.- Query Processing over Uncertain Data.- Query Processor.- Query Rewriting.- Query Rewriting Using Views.- Query Translation.- Quorum Systems.- Randomization Methods to Ensure Data Privacy.- Range Query.- Rank-aware Query Processing.- Ranked XML Processing.- Ranking Functions.- Ranking Views.- Rank-Join.- Rank-Join Indices.- Raster Data Management and Multi-Dimensional Arrays.- RDF Stores.- RDF Technology.- Real and Synthetic Test Datasets.- Real-Time Transaction Processing.- Recall.- Receiver Operating Characteristic.- Recommender Systems.- Record Linkage.- Record Matching.- Redundant Arrays of Independent Disks.- Reference Knowledge.- Region Algebra.- Regulatory Compliance in Data Management.- Relational Algebra.- Relational Calculus.- Relational Model.- Relationships in Structured Text Retrieval.- Relative Time.- Relevance.- Relevance Feedback.- Relevance Feedback for Content-Based Information Retrieval.- Relevance Feedback for Text Retrieval.- Replica Control.- Replica Freshness.- Replicated Data Types.- Replicated Database Concurrency Control.- Replication.- Replication Based on Group Communication.- Replication for Availability and Fault-Tolerance.- Replication for High Availability.- Replication for Paxos.- Replication for Scalability.- Replication in Multi-Tier Architectures.- Replication with Snapshot Isolation.- Reputation and Trust.- Request Broker.- Residuated Lattice.- Resource Allocation Problems in Spatial Databases.- Resource Description Framework.- Resource Description Framework (RDF) Schema (RDFS).- Resource Identifier.- Result Display.- Retrospective Event Processing.- Reverse Nearest Neighbor Query.- Reverse Top-k Queries.- Rewriting Queries using Views.- RMI.- Road Networks.- Rocchio's Formula.- Role Based Access Control.- R-Precision.- R-Tree (and Family).- Rule-based Classification.- Safety and Domain Independence.- Sagas.- Sampling Techniques for Statistical Databases.- SAN File System.- Scalable Decision Tree Construction.- Scheduler.- Scheduling Strategies for Data Stream Processing.- Schema Evolution.- Schema Mapping.- Schema Mapping Composition.- Schema Matching.- Schema Tuning.- Schema Versioning.- Scheme/Ontology Extraction.- Scientific Databases.- Scientific Visualization.- Scientific Workflows.- Score Aggregation.- Screen Scraper.- SCSI Target.- SDC Score.- Search Engine Metrics.- Searching Digital Libraries.- Second Normal Form (2NF).- Secondary Index.- Secure Data Outsourcing.- Secure Database Development.- Secure Multiparty Computation Methods.- Secure Transaction Processing.- Security Services.- Segmentation and Stratification.- Segmentation and Stratification_old.- Selection.- Selectivity Estimation.- Self-Maintenance of Views.- Self-Management Technology in Databases.- Semantic Atomicity.- Semantic Crowd Sourcing.- Semantic Data Integration for Life Science Entities.- Semantic Data Model.- Semantic Matching.- Semantic Modeling and Knowledge Representation for Multimedia Data.- Semantic Modeling for Geographic Information Systems.- Semantic Overlay Networks.- Semantic Social Web.- Semantic Streams.- Semantic Web.- Semantic Web Query Languages.- Semantic Web Services.- Semantics-based Concurrency Control.- Semijoin.- Semijoin Program.- Semi-Structured Data.- Semi-Structured Data Model.- Semi-Structured Database Design.- Semi-Structured Query Languages.- Semi-Supervised Learning.- Sensor Networks.- Sequenced Semantics.- Sequential Patterns.- Serializability.- Serializable Snapshot Isolation.- Service Component Architecture (SCA).- Service Oriented Architecture.- Session.- Shared-Disk Architecture.- Shared-Memory Architecture.- Shared-Nothing Architecture.- Side-Effect-Free View Updates.- Signature Files.- Similarity and Ranking Operations.- Simplicial Complex.- Singular Value Decomposition.- Skyline Queries and Pareto Optimality.- Snapshot Equivalence.- Snapshot Isolation.- Snippet.- Snowflake Schema.- SOAP.- Social Applications.- Social influence.- Social Media Analysis.- Social Media Analytics.- Social Media Harvesting.- Social network analysis.- Social Networks.- Software As-A-Service (SaaS).- Software Transactional Memory.- Software-Defined Storage.- Solid State Drive (SSD).- Sort-Merge Join.- Space-Filling Curves.- Space-Filling Curves for Query Processing.- SPARQL.- Sparse Index.- Spatial and Spatio-Temporal Data Models and Languages.- Spatial and Temporal Data Warehouses .- Spatial Anonymity.- Spatial Data Analysis.- Spatial Data Mining.- Spatial Data Types.- Spatial Datawarehousing.- Spatial Indexing Techniques.- Spatial Join.- Spatial Keyword Search.- Spatial Matching Problems.- Spatial Network Databases.- Spatial Operations and Map Operations.- Spatial Queries in the Cloud.- Spatio-Temporal Data Mining.- Spatio-Temporal Data Types.- Spatio-Temporal Data Warehouses.- Spatiotemporal Interpolation Algorithms.- Spatio-Temporal Selectivity Estimation.- Spatio-Temporal Trajectories.- Specialization and Generalization.- Specificity.- Spectral Clustering.- Split.- Split Transactions.- SQL.- SQL Analytics on Big Data.- SQL Isolation Levels.- SQL-Based Temporal Query Languages.- Stable Distribution.- Stack-based Query Language.- Staged DBMS.- Standard Effectiveness Measures.- Star Index.- Star Schema.- State-based Publish/Subscribe.- Statistical Data Management.- Statistical Disclosure Limitation For Data Access.- Steganography.- Stemming.- Stop-&-go Operator.- Stoplists.- Storage Access Models.- Storage Area Network.- Storage Consolidation.- Storage Devices.- Storage Grid.- Storage Management.- Storage Management Initiative-Specification.- Storage Manager.- Storage Network Architectures.- Storage Networking Industry Association.- Storage of Large Scale Multidimensional Data.- Storage Power Management.- Storage Protection.- Storage Protocols.- Storage Resource Management.- Storage Security.- Storage Virtualization.- Stored Procedure.- Stream Mining.- Stream Models.- Stream Processing.- Stream processing on modern hardware.- Stream Reasoning.- Stream Sampling.- Stream Similarity Mining.- Streaming Analytics.- Streaming Applications.- Stream-Oriented Query Languages and Operators.- Strong Consistency Models for Replicated Data.- Structural Indexing.- Structure Analytics in Social Media.- Structure Weight.- Structured Data in Peer-to-Peer Systems.- Structured Document Retrieval.- Structured Text Retrieval Models.- Subject Spaces.- Subspace Clustering Techniques.- Success at n.- Succinct Constraints.- Suffix Tree.- Summarizability.- Summarization.- Support Vector Machine.- Supporting Transaction Time Databases.- Symbolic Representation.- Symmetric Encryption.- Synopsis Structure.- Synthetic Microdata.- System R (R*) Optimizer.- Table.- Tabular Data.- Taxonomy: Biomedical Health Informatics.- tBench.- Telic Distinction in Temporal Databases.- Telos.- Temporal Access Control.- Temporal Aggregation.- Temporal Algebras.- Temporal Analytics in Social Media.- Temporal Benchmarks.- Temporal Coalescing.- Temporal Compatibility.- Temporal Conceptual Models.- Temporal Constraints.- Temporal Data Mining.- Temporal Data Models.- Temporal Database.- Temporal Datawarehousing.- Temporal Dependencies.- Temporal Element.- Temporal Expression.- Temporal Generalization.- Temporal Granularity.- Temporal Homogeneity.- Temporal Indeterminacy.- Temporal Integrity Constraints.- Temporal Joins.- Temporal Logic in Database Query Languages.- Temporal Logical Models.- Temporal Object-Oriented Databases.- Temporal Periodicity.- Temporal Projection.- Temporal PSM.- Temporal Query Languages.- Temporal Query Processing.- Temporal Relational Calculus.- Temporal Specialization.- Temporal Strata.- Temporal Support in the SQL Standard.- Temporal Vacuuming.- Temporal Visual Languages.- Temporal XML.- Term Proximity.- Term Statistics for Structured Text Retrieval.- Term Weighting.- Test Collection.- Text Analytics.- Text Analytics in Social Media.- Text Categorization.- Text Clustering.- Text Compression.- Text Generation.- Text Index Compression.- Text Indexing and Retrieval.- Text Indexing Techniques.- Text Mining.- Text Mining of Biological Resources.- Text Representation.- Text Segmentation.- Text Semantic Representation.- Text Stream Processing.- Text Streaming Model.- Text Summarization.- Text Visualization.- TF*IDF.- Thematic Map.- Third Normal Form.- Three-Dimensional GIS and Geological Applications.- Three-Phase Commit.- Tight Coupling.- Time Aggregated Graphs.- Time and Information Retrieval.- Time Domain.- Time in Philosophical Logic.- Time Instant.- Time Interval.- Time Period.- Time Series Query.- Time Span.- Time-Line Clock.- Timeslice Operator.- Topic Detection and Tracking.- Topic Maps.- Topic-based Publish/Subscribe.- Top-k Queries.- Top-K Selection Queries on Multimedia Datasets.- Topological Data Models.- Topological Relationships.- Trajectory.- Transaction.- Transaction Chopping.- Transaction Management.- Transaction Manager.- Transaction Models - the Read/Write Approach.- Transaction Time.- Transactional Middleware.- Transactional Processes.- Transactional Stream Processing.- Transaction-Time Indexing.- Tree-based Indexing.- Treemaps.- Triangular Norms.- Triangulated Irregular Network.- Trie.- Trip Planning Queries.- Trust and Reputation in Peer-to-Peer Systems.- Trust in Blogosphere.- Trusted Hardware.- TSQL2.- Tuning Concurrency Control.- Tuple-Generating Dependencies.- Two-Dimensional Shape Retrieval.- Two-Phase Commit.- Two-Phase Commit Protocol.- Two-Phase Locking.- Two-Poisson model.- Type-based Publish/Subscribe.- U-measure.- Uncertain Data Lineage.- Uncertain Data Mining.- Uncertain Data Models.- Uncertain Data Streams.- Uncertain Data Summarization.- Uncertain Graph Data Management.- Uncertain Spatial Data Management.- Uncertain Top-k Queries.- Uncertainty in Events.- Uncertainty Management in Scientific Database Systems.- Unicode.- Unified Modeling Language.- Union.- Unobservability.- Updates and Transactions in Peer-to-Peer Systems.- Updates through Views.- Usability.- User-Defined Time.- Valid Time.- Valid-Time Indexing.- Value Equivalence.- Variable Time Span.- Vector-Space Model.- Vertically Partitioned Data.- Video.- Video Content Analysis.- Video Content Modeling.- Video Content Structure.- Video Metadata.- Video Querying.- Video Representation.- Video Scene and Event Detection.- Video Segmentation.- Video Sequence Indexing.- Video Shot Detection.- Video Summarization.- View Adaptation.- View Definition.- View Maintenance.- View Maintenance Aspects.- View-based Data Integration.- Views.- Virtual Partitioning.- Visual Analytics.- Visual Association Rules.- Visual Classification.- Visual Clustering.- Visual Content Analysis.- Visual Data Mining.- Visual Formalisms.- Visual Interaction.- Visual Interfaces.- Visual Interfaces for Geographic Data.- Visual interfaces for streaming data.- Visual Metaphor.- Visual On-Line Analytical Processing (OLAP).- Visual Perception.- Visual Query Language.- Visual Representation.- Visualization for Information Retrieval.- Visualization Pipeline.- Visualizing Categorical Data.- Visualizing Clustering Results.- Visualizing Hierarchical Data.- Visualizing Network Data.- Visualizing Quantitative Data.- Volume.- Voronoi Diagrams.- W3C.- WAN Data Replication.- Wavelets on Streams.- Weak Consistency Models for Replicated Data.- Weak Equivalence.- Web 2.0/3.0.- Web Advertising.- Web Characteristics and Evolution.- Web Crawler Architecture.- Web Data Extraction System.- Web ETL.- Web Harvesting.- Web Information Extraction.- WEB Information Retrieval Models.- Web Mashups.- Web Page Quality Metrics.- Web Question Answering.- Web Search Query Rewriting.- Web Search Relevance Feedback.- Web Search Relevance Ranking.- Web Search Result Caching and Prefetching.- Web Search Result De-duplication and Clustering.- Web Services.- Web Services and the Semantic Web for Life Science Data.- Web Spam Detection.- Web Transactions.- Web Views.- What-If Analysis.- WIMP Interfaces.- Window operator in RDBMS.- Window-based Query Processing.- Windows.- Workflow Constructs.- Workflow Evolution.- Workflow Join.- Workflow Management.- Workflow Management and Workflow Management System.- Workflow Management Coalition.- Workflow Model.- Workflow Model Analysis.- Workflow Patterns.- Workflow Schema.- Workflow Transactions.- Wrapper Induction.- Wrapper Maintenance.- Wrapper Stability.- Write Once Read Many.- XML.- XML Access Control.- XML Attribute.- XML Benchmarks.- XML Compression.- XML Document.- XML Element.- XML Indexing.- XML Information Integration.- XML Integrity Constraints.- XML Metadata Interchange.- XML Metadata Interchange Specification (XMI).- XML Parsing, SAX/DOM.- XML Process Definition Language.- XML Programming.- XML Publish/Subscribe.- XML Publishing.- XML Retrieval.- XML Schema.- XML Selectivity Estimation.- XML Storage.- XML Stream Processing.- XML Tree Pattern, XML Twig Query.- XML Tuple Algebra.- XML Typechecking.- XML Types.- XML Updates.- XML Views.- XPath/XQuery.- XQuery Full-Text.- XQuery Processors.- XSL/XSLT.- Zero-One Laws.- Zooming Techniques.- α-nDCG.-
£4,422.28
APress Mastering Snowflake Solutions
Book SynopsisDesign for large-scale, high-performance queries using Snowflake's query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis.Mastering Snowflake Solutionsstarts with a refresher on Snowflake's unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater Table of Contents1. Snowflake Architecture2. Data Movement3. Cloning4. Managing Security and User Access Control 5. Protecting Data in Snowflake6. Business Continuity and Disaster Recovery7. Data Sharing and the Data Cloud8. Programming9. Advanced Performance Tuning10. Developing Applications in Snowflake
£46.74
APress Building the Snowflake Data Cloud
Book SynopsisImplement the Snowflake Data Cloud using best practices and reap the benefits of scalability and low-cost from the industry-leading, cloud-based, data warehousing platform. This book provides a detailed how-to explanation, and assumes familiarity with Snowflake core concepts and principles. It is a project-oriented book with a hands-on approach to designing, developing, and implementing your Data Cloud with security at the center. As you work through the examples, you will develop the skill, knowledge, and expertise to expand your capability by incorporating additional Snowflake features, tools, and techniques. Your Snowflake Data Cloud will be fit for purpose, extensible, and at the forefront of both Direct Share, Data Exchange, and Snowflake Marketplace. Building the Snowflake Data Cloud helps you transform your organization into monetizing the value locked up within your data. As the digital economy takes hold, with data volume, velociTable of ContentsPart I. Context 1. The Snowflake Data Cloud 2. Breaking Data Siloes Part II. Concepts 3. Architecture 4. Account Security5. Role Based Access Control (RBAC)6. Account Usage StorePart III. Tools7. Ingesting Data8. Data Pipelines9. Data Presentation10. Semi Structured and Unstructured DataPart IV. Management11. Query Optimizer Basics12. Data Management13. Data Modelling14. Snowflake Data Cloud By Example
£46.74
APress Data Science and Analytics for SMEs
Book SynopsisMaster the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business'' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science Trade Review“By reading the book and working out the use case, subject matter experts will be able to get a coherent roadmap to the main techniques available for both descriptive and predictive data analytics, as well as be able to provide simple services related to their company data and future prospects.” (Rosario Uceda-Sosa, Computing Reviews, October 2, 2023)Table of Contents INTRODUCTIONWe introduce data science generally and narrow it down to data science for business which is also referred to as business analytics. We then give a detailed explanation of the process involved in business analytics in form of the business analytics journey. In this journey, we explain what it takes from start to finish to carry out an analytics project in the business world, focusing on small business consulting, even though the process is generic to all types of business, small or large. We also give a description of what small business refers to in this book and the peculiarities of navigating an analytics project in such a terrain. To conclude the chapter, we talk about the types of analytics problems that is common to small business and the tools available to solve these problems given the budget situation of small businesses when it comes to analytics project.· DATA SCIENCE· DATA SCIENCE FOR BUSINESS· BUSINESS ANALYTICS JOURNEY· SMALL AND MEDIUM BUSINESS (SME)· BUSINESS ANALYTICS IN SMALL BUSINESS· TYPES OF ANALYTICS PROBLEMS IN SME· ANALYTICS TOOLS FOR SMES· ROAD MAPS TO THIS BOOK· PROBLEMS· REFERENCES CHAPTER 1: DATA FOR ANALYSIS IN SMALL BUSINESSIn this chapter, we would look at the various sources of data generally and in small business. This chapter is important because the major challenge of consulting for small business is the lack of data or quality data for analysis. This chapter will therefore detail the sources of data for analysis explaining first the type or form that data exists and some general ideas of how to collect such data. It gives an overview on data quality and integrity issues and touches on data literacy. The chapter also includes the typical data preparation procedures for the common types of techniques used in small business analytics and by extension used in this book. To conclude the chapter, we look at data visualization, particularly towards preparing data for various analytics task as explained in section 1.3.· SOURCE OF DATA· DATA QUALITY & INTEGRITY· DATA GOVERNANCE· DATA PREPARATION· DATA VISUALIZATION· PROBLEMS· REFERENCESCHAPTER 2: BUSINESS ANALYTICS CONSULTINGIn this chapter, we will look at business analytics consulting, particularly what the concept implies and how to build such a career path. We will explain the types of business analytics consulting that exist and then narrow it down to how to navigate the world of business analytics consulting for small business. In this chapter, we will look at how to manage a typical analytics project and measure the success of analytics projects. In conclusion, we will discuss issues revolving around how to bill analytics project particularly as a consultant.· BUSINESS ANALYTICS CONSULTING· MANAGING ANALYTICS PROJECT· SUCCESS METRICS IN ANALYTICS PROJECT· BILLING ANALYTICS PROJECT· PROBLEMS· REFERENCESCHAPTER 3: BUSINESS ANALYTICS CONSULTING PHASESIn this chapter we will look at the stages involved business analytics consulting, particularly when the analytics service is offered as a product from either within or outside the business. We will look at the proposal and initial analysis stage which gives direction to the analytics project. Then we look at the details involved in the pre-engagement, engagement and post engagement phase. It is important to know that the stages are presented in a typical or generic way but when implemented, there might be reason to modify or customize them for the application scenario.· PROPOSAL & INITIAL ANALYSIS· PRE- ENGAGEMENT PHASE· ENGAGEMENT PHASE· POST ENGAGEMENT PHASE· PROBLEMS· REFERENCES CHAPTER 4: DESCRIPTIVE ANALYTICS TOOLSThis chapter is focused on the mostly common descriptive analytics tools used in business generally and specifically in small businesses. The chapter will help to use descriptive analytics tools to understand your business and make recommendations that can improve your business profits. For small business, descriptive analytics helps SMEs to make sense of available data in order to monitor business indicators at a glance, helps SME owners to observe sales trends and patterns on an overall basis, as well as deep-dive into product categories and customer groups. It also helps SME’s to plan product strategy, pricing policies that will maximize their projected revenues and derive a lot of valuable insights for getting more customers. · INTRODUCTION· BAR CHART· HISTOGRAM· LINE GRAPHS· SCATTER PLOTS· PACKED BUBBLES CHARTS· HEAT MAPS· GEOGRAPHICAL MAPS· A PRACTICAL BUSINESS PROBLEM I· PROBLEMS· REFERENCES CHAPTER 5: PREDICTION TECHNIQUESIn this chapter, we will explore the popular techniques used for prediction, particularly in retails business. The approach used in explaining these techniques us to use them in solving a business problem. The second business problem to be addressed is the sales prediction problem which is common in retail business. The chapter first explain the fundamental concept of prediction techniques, next we look at how such techniques are evaluated. After this, we describe the business problem we intend solving. We then pick each of the selected techniques one by one and explain the algorithms involved and how they can be used to solve the problem described. The prediction techniques used and compared are the Multiple linear regression, the Regression Trees and the Neural Network. To conclude the chapter, we compare the results of the three algorithms and conclude on the problem in question. In this chapter therefore, the analytics products being offered is to solve sales prediction problem for small retail business.· INTRODUCTION· PRACTICAL BUSINESS PROBLEM II (SALES PREDICTION)· MULTIPLE LINEAR REGRESSION· REGRESSIN TREES· NEURAL NETWORK (PREDICTION)· CONCLUSION ON SALES PREDICTION· PROBLEMS· REFERENCES CHAPTER 6: CLASSIFICATION TECHNIQUESIn this chapter, even though there are several classification techniques, we will explore the popular ones used for classification in the business domain. In doing this, we will use the third business problem centered on customer loyalty comparing neural network, classification tree and random forest algorithms. In solving this problem, we are particular about how to get and retain more customers for our small business. We will also introduce some other classification based techniques such as K-nearest neighbour logistic regression and persuasion modelling. We will use persuasion modelling for the fourth practical business problem. In using these techniques to solve the problem we explain the fundamental concepts in the chosen algorithms and use them to demonstrate how this problems solving process can be adopted in real business scenarios.· CLASSIFICATION MODELS & EVALUATION· PRACTICAL BUSINESS PROBLEM III (CUSTOMER LOYALTY)· NEURAL NETWORK· CLASSIFICATION TREE· RANDOM FOREST & BOOSTED TREES· K NEAREST NEIGHBOUR· LOGISTIC REGRESSION· PROBLEMS· REFERENCES CHAPTER 7: ADVANCED DESCRIPTIVE ANALYTICSThis chapter is focused mainly on advanced descriptive analytics techniques. In this chapter, we will first explain the concept of clustering which is a type of unsupervised learning approach. We will then pick one clustering technique which is the K means clustering. Using the fourth practical business problem, we will explain how we can use the K means clustering technique to solve a real business problem. Next will explain the association rule example and finally Network analysis. We conclude with the fifth business problem which is focused on using network analytics for employee efficiency.· CLUSTERING· K MEANS· PRACTICAL BUSINESS PROBLEM IV (Customer Segmentation)· ASSOCIATION ANALYSIS· NETWORK ANALYSIS· PRACTICAL BUSINESS PROBLEM V (Staff Efficiency)· PROBLEMS· REFERENCES CHAPTER 8: CASE STUDY PART IThis chapter is the beginning part of major consulting case study for this book. We will explain what transpired during a typical business analytics consulting and help to create a road map or an example of how to navigate a business analytics consulting project. We start with a description of the SME Ecommerce environment generally, since this is the business environment of our selected case study, we then talk about the sources of data for analytics peculiar this environment. Next we describe the business to be used as case study briefly, followed by the analytics road map peculiar to consulting for this business. This chapter ends with the results of the initial analysis and pre engagement phase which forms the bases for the detailed analytics and implementation phase in chapter 10.· SME ECORMERCE· INTRODUCTION TO SME CASE STUDY· INITIAL ANALYSIS· ANALYTICS APPROACH · PRE –ENGAGEMENT· PROBLEMS· REFERENCES CHAPTER 9: CASE STUDY PART IIIn this chapter, we will conclude the case study used for illustration of a typical business analytics consulting for an SME by presenting the details of the engagement phase for the case study in question. The post engagement phase is left out as the implementation of the recommendations is determined by the systems and procedures of the business. It is important to note that the consulting steps can be customized for any small business based on the intended problem. The whole steps described in chapter 9 and 10 have been made simple for understanding, though in real life business application there might be need to iterate the process until satisfactory results have been gotten. This is because you constantly need to incorporate feedback from the stakeholders and domain experts.· GOAL 1: INCREASE WEBSITE TRAFFIC· GOAL 2: INCREASE WEBSITE SALES REVENUE· PROBLEMS· REFERENCES
£31.34
APress Google Cloud Platform for Data Science
Book SynopsisThis book is your practical and comprehensive guide to learning Google Cloud Platform (GCP) for data science, using only the free tier services offered by the platform. Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. GCP offers a range of data science services that can be used to store, process, and analyze large datasets, and train and deploy machine learning models. The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples illustrating how to use GCP services for data science and big data projects. Readers will learn how to set up a Google Colaboratory account and run Jupyternotebooks, access GCP services and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL. What You Will LearnSet up a GCP account and projectExplore BigQuery and its use cases, including machine learningUnderstand Google Cloud AI Platform and its capabilities Use Vertex AI for training and deploying machine learning modelsExplore Google Cloud Dataproc and its use cases for big data processingCreate and share data visualizations and reports with Looker Data StudioExplore Google Cloud Dataflow and its use cases for batch and stream data processing Run data processing pipelines on Cloud DataflowExplore Google Cloud Storageand its use cases for data storage Get an introduction to Google Cloud SQL and its use cases for relational databases Get an introduction to Google Cloud Pub/Sub and its use cases for real-time data streamingWho This Book Is ForData scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projectsTable of ContentsChapter 1: Introduction to GCP.- Chapter 2: Google Colaboratory.- Chapter 3: Big Data and Machine Learning.- Chapter 4: Data Visualization and Business Intelligence.- Chapter 5: Data Processing and Transformation.- Chapter 6: Data Analytics and Storage.- Chapter 7: Advanced Topics.
£38.24
O'Reilly Media Big Data for Chimps
Book SynopsisFinding patterns in massive event streams can be difficult, but learning how to find them doesn't have to be. This unique hands-on guide shows you how to solve this and many other problems in large-scale data processing with simple, fun, and elegant tools that leverage Apache Hadoop.
£25.59
O'Reilly Media Learning to Love Data Science
Book SynopsisToday, big data is taken seriously, and data science is considered downright sexy. With this anthology of reports from award-winning journalist Mike Barlow, you'll appreciate how data science is fundamentally altering our world, for better and for worse.
£16.99
O'Reilly Media Agile Data Science 2.0
Book SynopsisWith the revised second edition of this hands-on guide, up-and-coming data scientists will learn how to use the Agile Data Science development methodology to build data applications with Python, Apache Spark, Kafka, and other tools.
£35.99
O'Reilly Media Getting Started with Kudu
Book SynopsisWith this practical guide, you'll learn how Kudu's architecture and features solve a unique problem in the Hadoop ecosystem. If you're familiar with other storage layer projects such HDFS, HBase, Spanner, and Cassandra, you'll quickly learnand appreciatethe unique contribution Kudu makes to this ecosystem.
£29.99
O'Reilly Media The Practitioners Guide to Graph Data
Book SynopsisGraph data closes the gap between the way humans and computers view the world. While computers rely on static rows and columns of data, people navigate and reason about life through relationships. This practical guide demonstrates how graph data brings these two approaches together.
£47.99
O'Reilly Media Mastering Spark with R
Book SynopsisWith this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems.
£33.74
O'Reilly Media Applied Natural Language Processing in the
Book SynopsisThis hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.
£51.74
Information Age Publishing A Primer on Business Analytics: Perspectives from
Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
£44.96
Information Age Publishing A Primer on Business Analytics: Perspectives from
Book SynopsisThis book will provide a comprehensive overview of business analytics, for those who have either a technical background (quantitative methods) or a practitioner business background. Business analytics, in the context of the 4th Industrial Revolution, is the "new normal" for businesses that operate in this digital age. This book provides a comprehensive primer and overview of the field (and related fields such as Business Intelligence and Data Science). It will discuss the field as it applies to financial institutions, with some minor departures to other industries. Readers will gain understanding and insight into the field of data science, including traditional as well as emerging techniques. Further, many chapters are dedicated to the establishment of a data-driven team – from executive buy-in and corporate governance to managing and quantifying the return of data-driven projects.
£82.80
Information Age Publishing Contemporary Perspectives in Data Mining
Book SynopsisThe series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.)
£44.96
Information Age Publishing Contemporary Perspectives in Data Mining
Book SynopsisThe series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner.Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups.Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.)
£82.80
John Wiley & Sons Inc New Challenges for Knowledge: Digital Dynamics to
Book SynopsisDigital technologies are reshaping every field of social and economic lives, so do they in the world of scientific knowledge. “The New Challenges of Knowledge” aims at understanding how the new digital technologies alter the production, diffusion and valorization of knowledge. We propose to give an insight into the economical, geopolitical and political stakes of numeric in knowledge in different countries. Law is at the center of this evolution, especially in the case of national and international confusion about Internet, Science and knowledge.Trade Review“Sharing economy models are rippling through the world of scientific knowledge and research; open access brings challenges for developers, researchers, and policy makers – all treated here in the context of law-making” The Magpi, issue 60, Aug 2017Table of ContentsIntroduction . xiii Part 1. Production: Global Knowledge and Science in the Digital Era 1 Chapter 1. Current Knowledge Dynamics 3 1.1. Transparency of scientific data 4 1.2. Transparency of experimental protocol 6 1.3. A necessary form of research engineering 7 1.4. Confusion between data and scientific results: avoiding manipulation of research results 8 Chapter 2. Digital Conditions for Knowledge Production 11 2.1. An economic system oriented toward innovation 11 2.2. What of knowledge and indeed the concept of the commons? 13 2.3. From analog to digital 14 2.4. User–producer: civil society enters the knowledge production system 16 2.5. The interactions between the various spheres of knowledge production 18 2.6. Collaboration between society and knowledge: producing authorities should be put into perspective 20 Chapter 3. The Dual Relationship between the User and the Developer 23 3.1. Legal arrangements for knowledge-sharing using development platforms 23 3.2. The user contributes to the creation and development of content process 25 Chapter 4. Researchers’ Uses and Needs for Scientific and Technical Information 29 4.1. The CNRS survey 29 4.2. Diverse uses and dual needs 31 4.3. An explanation through differentiated scientific analysis 33 Chapter 5. New Tools for Knowledge Capture 37 5.1. The growth of metadata exploitation 37 5.2. Are we moving toward a semantic Web? 38 5.3. Tools and limits for metadata processing 39 5.4. The challenges of the semantic Web 40 Chapter 6. Modes of Knowledge Sharing and Technologies 43 6.1. Data storage technologies and access allowing knowledge sharing 43 6.2. Exchange platforms and catalogs 44 6.3. Knowledge-processing and digital editions 45 Part 2. Sharing Mechanisms: Knowledge Sharing and the Knowledge-based Economy 47 Chapter 7. Business Model for Scientific Publication 49 7.1. The current economic model is changing so as to adapt to new conditions for knowledge sharing 49 7.2. Creation of a new model 51 7.3. The issues raised by the creation of a new economic model 52 7.4. A new economic model struggling to fine its niche 54 Chapter 8. Actor Strategy: International Scientific Publishing, Services with High Added Value and Research Communities 57 8.1. Publishing, editing and existing: live issues within the publication of Scientific and Technical Information (STI) 58 8.2. Who is subject to it? The other players in scientific publishing 59 8.3. The characteristics of SMS (Science of Man and Society) 60 8.4. Existing without publishing? New STI directions 62 8.5. Alternatives to scientific publishing 63 Chapter 9. New Approaches to Scientific Production 67 9.1. New means of access to scientific production: innovative models 67 9.2. Two main objectives: accelerating knowledge sharing and promoting scientific collaboration 71 9.3. The need for new analytical tools and the risk of reprivatization of scientific knowledge. 72 9.4. The absence of the usage doctrine and the risk of reprivatization of science: the case of social networks 74 Chapter 10. The Geopolitics of Science 77 10.1. National convergent research models 78 10.2. Science is a source of international cooperation 81 10.3. International scientific cooperation is accelerating 84 Chapter 11. Copyright Serving the Market 85 Part 3. Enhancement Knowledge Rights and Public Policies in the Wake of Digital Technology 89 Chapter 12. Legal Protection of Scientific Research Results in the Humanities and Social Sciences 91 12.1.Different legal protections for different kinds of science 91 12.2. Why protect? 92 12.3. How to protect 93 12.4. Protect against whom? 98 12.5. Changing the challenges of Internet protection 99 12.6. Legal obstacles related to the author’s right 100 Chapter 13. Development of Knowledge and Public Policies 103 13.1. Knowledge enhancement concerns everyone 104 13.2. What are the public policies for enhancing knowledge? 105 13.3. State establishment of connections between actors: a key tool in knowledge enhancement 107 13.4. Comparing the United States and the European Union 109 Chapter 14. From Author to Enhancer 111 14.1. Enhancing scientific research is a complex process 112 14.2. Scientific research enhancement follows a legislative framework intended to promote innovation 114 Chapter 15. The Right to Knowledge: Moving Toward a Universal Law? 117 15.1. Unclear regulatory frameworks 118 15.2. Developing legal frameworks related to the Internet is complicated 121 15.3. Proposals for developing legal frameworks for the Internet 123 Chapter 16. Governing by Algorithm 127 16.1. Statistics that foreshadow algorithms 128 16.2. Algorithmic governance and democratic opportunities 130 Chapter 17. Public Data and Science in e-Government 133 17.1. Disseminating data and disseminating science: a new requirement 134 17.2. Public data in the e-government 137 17.3. Science within e-government 139 Chapter 18. Surveillance, Sousveillance, Improper Capturing 141 18.1. The traditional legal framework for information capture 142 18.2. The clear need for a specific law 145 Chapter 19. Public Knowledge Policies in the Digital Age 149 19.1. GAFA domination and the oligopolization of the market 150 19.2. Isolated digital ecosystems 152 19.3. Regulation through competition law 153 19.4. Data protection: moving toward a law for the digital community 154 Chapter 20. The Politics of Creating Artificial Intelligence 157 20.1. History 158 20.2. Artificial intelligence has become a priority for public and private actors 160 20.4. The appearance of legal problems 162 Chapter 21. Security Policies in Artificial Intelligence 165 21.1. Security as a comment on machines and data 166 21.2. From the security of machines to the security of humans 169 Conclusion 175 Postscript 177 Glossary 179 Bibliography 185 Index 201
£125.06
ISTE Ltd and John Wiley & Sons Inc Argument Mining: Linguistic Foundations
Book SynopsisThis book is an introduction to the linguistic concepts of argumentation relevant for argument mining, an important research and development activity which can be viewed as a highly complex form of information retrieval, requiring high-level natural language processing technology. While the first four chapters develop the linguistic and conceptual aspects of argument expression, the last four are devoted to their application to argument mining. These chapters investigate the facets of argument annotation, as well as argument mining system architectures and evaluation. How annotations may be used to develop linguistic data and how to train learning algorithms is outlined. A simple implementation is then proposed. The book ends with an analysis of non-verbal argumentative discourse. Argument Mining is an introductory book for engineers or students of linguistics, artificial intelligence and natural language processing. Most, if not all, the concepts of argumentation crucial for argument mining are carefully introduced and illustrated in a simple manner.Table of ContentsPreface xi Chapter 1. Introduction and Challenges 1 1.1. What is argumentation? 1 1.2. Argumentation and argument mining 4 1.3. The origins of argumentation 7 1.4. The argumentative discourse 8 1.5. Contemporary trends 10 Chapter 2. The Structure of Argumentation 13 2.1. The argument–conclusion pair 13 2.2. The elementary argumentative schema 14 2.2.1. Toulmin’s argumentative model 14 2.2.2. Some elaborations and refinements of Toulmin’s model 17 2.2.3. The geometry of arguments 18 2.3. Modeling agreement and disagreement 20 2.3.1. Agreeing versus disagreeing 20 2.3.2. The art of resolving divergences 23 2.4. The structure of an argumentation: argumentation graphs 25 2.5. The role of argument schemes in argumentation 27 2.5.1. Argument schemes: main concepts 27 2.5.2. A few simple illustrations 28 2.5.3. Argument schemes based on analogy 29 2.5.4. Argument schemes based on causality 30 2.6. Relations between Toulmin’s model and argumentation schemes 31 2.6.1. Warrants as a popular opinion 32 2.6.2. Argument schemes based on rules, explanations or hypothesis 34 2.6.3. Argument schemes based on multiple supports or attacks 35 2.6.4. Causality and warrants 37 Chapter 3. The Linguistics of Argumentation 39 3.1. The structure of claims 40 3.2. The linguistics of justifications 45 3.3. Evaluating the strength of claims, justifications and arguments 47 3.3.1. Strength factors within a proposition 49 3.3.2. Structuring expressions of strength by semantic category 51 3.3.3. A simple representation of strength when combining several factors 52 3.3.4. Pragmatic factors of strength expression 53 3.4. Rhetoric and argumentation 59 3.4.1. Rhetoric and communication 60 3.4.2. Logos: the art of reasoning and of constructing demonstrations 61 3.4.3. Ethos: the orator profile 62 3.4.4. Pathos: how to persuade an audience 63 Chapter 4. Advanced Features of Argumentation for Argument Mining 65 4.1. Managing incoherent claims and justifications 65 4.1.1. The case of justifications supporting opposite claims 66 4.1.2. The case of opposite justifications justifying the same claim 67 4.2. Relating claims and justifications: the need for knowledge and reasoning 67 4.2.1. Investigating relatedness via corpus analysis 68 4.2.2. A corpus analysis of the knowledge involved 69 4.2.3. Observation synthesis 72 4.3. Argument synthesis in natural language 74 4.3.1. Features of a synthesis 75 4.3.2. Structure of an argumentation synthesis 76 Chapter 5. From Argumentation to Argument Mining 79 5.1. Some facets of argument mining 79 5.2. Designing annotation guidelines: some methodological elements 81 5.3. What results can be expected from an argument mining system? 82 5.4. Architecture of an argument mining system 83 5.5. The next chapters 84 Chapter 6. Annotation Frameworks and Principles of Argument Analysis 85 6.1. Principles of argument analysis 86 6.1.1. Argumentative discourse units 86 6.1.2. Conclusions and premises 88 6.1.3. Warrants and backings 89 6.1.4. Qualifiers 89 6.1.5. Argument schemes 90 6.1.6. Attack relations: rebuttals, refutations, undercutters 90 6.1.7. Illocutionary forces, speech acts 92 6.1.8. Argument relations 93 6.1.9. Implicit argument components and tailored annotation frameworks 95 6.2. Examples of argument analysis frameworks 97 6.2.1. Rhetorical Structure Theory 97 6.2.2. Toulmin’s model 98 6.2.3. Inference Anchoring Theory 99 6.2.4. Summary 102 6.3. Guidelines for argument analysis 103 6.3.1. Principles of annotation guidelines 103 6.3.2. Inter-annotator agreements 104 6.3.3. Interpretation of IAA measures 105 6.3.4. Some examples of IAAs 106 6.3.5. Summary 107 6.4. Annotation tools 108 6.4.1. Brat 108 6.4.2. RST tool 109 6.4.3. AGORA-net 110 6.4.4. Araucaria 110 6.4.5. Rationale 111 6.4.6. OVA+ 112 6.4.7. Summary 113 6.5. Argument corpora 114 6.5.1. COMARG 115 6.5.2. A news editorial corpus 115 6.5.3. THF Airport ArgMining corpus 115 6.5.4. A Wikipedia articles corpus 115 6.5.5. AraucariaDB 115 6.5.6. An annotated essays corpus 116 6.5.7. A written dialogs corpus 116 6.5.8. A web discourse corpus 116 6.5.9. Argument Interchange Format Database 116 6.5.10. Summary 117 6.6. Conclusion 118 Chapter 7. Argument Mining Applications and Systems 119 7.1. Application domains for argument mining 119 7.1.1. Opinion analysis augmented by argument mining 120 7.1.2. Summarization 120 7.1.3. Essays 120 7.1.4. Dialogues 120 7.1.5. Scientific and news articles 120 7.1.6. The web 121 7.1.7. Legal field 121 7.1.8. Medical field 121 7.1.9. Education 121 7.2. Principles of argument mining systems 122 7.2.1. Argumentative discourse units detection 123 7.2.2. Units labeling 123 7.2.3. Argument structure detection 124 7.2.4. Argument completion 125 7.2.5. Argument structure representation 125 7.3. Some existing systems for argument mining 126 7.3.1. Automatic detection of rhetorical relations 126 7.3.2. Argument zoning 126 7.3.3. Stance detection 127 7.3.4. Argument mining for persuasive essays 127 7.3.5. Argument mining for web discourse 127 7.3.6. Argument mining for social media 128 7.3.7. Argument scheme classification and enthymemes reconstruction 128 7.3.8. Argument classes and argument strength classification 128 7.3.9. Textcoop 129 7.3.10. IBM debating technologies 129 7.3.11. Argument mining for legal texts 129 7.4. Efficiency and limitations of existing argument mining systems 130 7.5. Conclusion 131 Chapter 8. A Computational Model and a Simple Grammar-Based Implementation 133 8.1. Identification of argumentative units 134 8.1.1. Challenges raised by the identification of argumentative units 134 8.1.2. Some linguistic techniques to identify ADUs 135 8.2. Mining for claims 139 8.2.1. The grammar formalisms 140 8.2.2. Lexical issues 142 8.2.3. Grammatical issues 145 8.2.4. Templates for claim analysis 148 8.3. Mining for supports and attacks 150 8.3.1. Structures introduced by connectors 150 8.3.2. Structures introduced by propositional attitudes 151 8.3.3. Other linguistic forms to express supports or attacks 152 8.4. Evaluating strength 153 8.5. Epilogue 154 Chapter 9. Non-Verbal Dimensions of Argumentation: a Challenge for Argument Mining 155 9.1. The text and its additions 156 9.1.1. Text, pictures and icons 156 9.1.2. Transcriptions of oral debates 156 9.2. Argumentation and visual aspects 157 9.3. Argumentation and sound aspects 158 9.3.1. Music and rationality 159 9.3.2. Main features of musical structure: musical knowledge representation 160 9.4. Impact of non-verbal aspects on argument strength and on argument schemes 161 9.5. Ethical aspects 162 Bibliography 163 Index 175
£125.06
ISTE Ltd and John Wiley & Sons Inc Sharing Economy and Big Data Analytics
Book SynopsisThe different facets of the sharing economy offer numerous opportunities for businesses ? particularly those that can be distinguished by their creative ideas and their ability to easily connect buyers and senders of goods and services via digital platforms. At the beginning of the growth of this economy, the advanced digital technologies generated billions of bytes of data that constitute what we call Big Data. This book underlines the facilitating role of Big Data analytics, explaining why and how data analysis algorithms can be integrated operationally, in order to extract value and to improve the practices of the sharing economy. It examines the reasons why these new techniques are necessary for businesses of this economy and proposes a series of useful applications that illustrate the use of data in the sharing ecosystem.Table of ContentsPreface xi Introduction xiii Part 1. The Sharing Economy or the Emergence of a New Business Model 1 Chapter 1. The Sharing Economy: A Concept Under Construction 3 1.1. Introduction 3 1.2. From simple sharing to the sharing economy 5 1.2.1. The genesis of the sharing economy and the break with “consumer” society 5 1.2.2. The sharing economy: which economy? 8 1.3. The foundations of the sharing economy 10 1.3.1. Peer-to-peer (P2P): a revolution in computer networks 10 1.3.2. The gift: the abstract aspect of the sharing economy 13 1.3.3. The service economy and the offer of use 18 1.4. Conclusion 24 Chapter 2. An Opportunity for the Business World 25 2.1. Introduction 25 2.2. Prosumption: a new sharing economy trend for the consumer 27 2.3. Poverty: a target in the spotlight of the shared economy 29 2.4. Controversies on economic opportunities of the sharing economy 31 2.5. Conclusion 37 Chapter 3. Risks and Issues of the Sharing Economy 39 3.1. Introduction 39 3.2. Uberization: a white grain or just a summer breeze? 40 3.3. The sharing economy: a disruptive model 43 3.4. Major issues of the sharing economy 47 3.5. Conclusion 50 Chapter 4. Digital Platforms and the Sharing Mechanism 51 4.1. Introduction 51 4.2. Digital platforms: “What growth!” 52 4.3. Digital platforms or technology at the service of the economy 54 4.4. From the sharing economy to the sharing platform economy 57 4.5. Conclusion 59 Part 2. Big Data Analytics at the Service of the Sharing Economy 61 Chapter 5. Beyond the Word “Big”: The Changes 63 5.1. Introduction 63 5.2. The 3 Vs and much more: volume, variety, velocity 64 5.2.1. Volume 65 5.2.2. The variety 66 5.2.3. Velocity 67 5.2.4. What else? 68 5.3. The growth of computing and storage capacities 69 5.3.1. Big Data versus Big Computing 70 5.3.2. Big Data storage 71 5.3.3. Updating Moore’s Law 73 5.4. Business context change in the era of Big Data 74 5.4.1. The decision-making process and the dynamics of value creation 75 5.4.2. The emergence of new data-driven business models 77 5.5. Conclusion 78 Chapter 6. The Art of Analytics 81 6.1. Introduction 81 6.2. From simple analysis to Big Data analytics 82 6.2.1. Descriptive analysis: learning from past behavior to influence future outcomes 84 6.2.2. Predictive analysis: analyzing data to predict future outcomes 84 6.2.3. Prescriptive analysis: recommending one or more action plan(s) 85 6.2.4. From descriptive analysis to prescriptive analysis: an example 87 6.3. The process of Big Data analytics: from the data source to its analysis 88 6.3.1. Definition of objectives and requirements 90 6.3.2. Data collection 91 6.3.3. Data preparation 92 6.3.4. Exploration and interpretation 94 6.3.5. Modeling 95 6.3.6. Deployment 97 6.4. Conclusion 97 Chapter 7. Data and Platforms in the Sharing Context 99 7.1. Introduction 99 7.2. Pioneers in Big Data 101 7.2.1. Big Data on Walmart’s shelves 101 7.2.2. The Big Data behind Netflix’s success story 102 7.2.3. The Amazon version of Big Data 103 7.2.4. Big data and social networks: the case of Facebook 104 7.2.5. IBM and data analysis in the health sector 105 7.3. Data, essential for sharing 106 7.3.1. Data and platforms at the heart of the sharing economy 108 7.3.2. The data of sharing economy companies 110 7.3.3. Privacy and data security in a sharing economy 111 7.3.4. Open Data and platform data sharing 114 7.4. Conclusion 116 Chapter 8. Big Data Analytics Applied to the Sharing Economy 119 8.1. Introduction 119 8.2. Big Data and Machine Learning algorithms serving the sharing economy 121 8.2.1. Machine Learning algorithms 122 8.2.2. Algorithmic applications in the sharing economy context 124 8.3. Big Data technologies: the sharing economy companies’ toolbox 125 8.3.1. The appearance of a new concept and the creation of new technologies 127 8.4. Big Data on the agenda of sharing economy companies 130 8.4.1. Uber 131 8.4.2. Airbnb 132 8.4.3. BlaBlaCar 133 8.4.4. Lyft 134 8.4.5. Yelp 135 8.4.6. Other cases 137 8.5. Conclusion 139 Part 3. The Sharing Economy? Not Without Big Data Algorithms 141 Chapter 9. Linear Regression 143 9.1. Introduction 143 9.2. Linear regression: an advanced analysis algorithm 144 9.2.1. How are regression problems identified? 145 9.2.2. The linear regression model 146 9.2.3. Minimizing modeling error 148 9.3. Other regression methods 149 9.3.1. Logistic regression 150 9.3.2. Additional regression models: regularized regression 151 9.4. Building your first predictive model: a use case 152 9.4.1. What variables help set a rental price on Airbnb? 152 9.5. Conclusion 169 Chapter 10. Classification Algorithms 171 10.1. Introduction 171 10.2. A tour of classification algorithms 172 10.2.1. Decision trees 172 10.2.2. Naïve Bayes 175 10.2.3. Support Vector Machine (SVM) 177 10.2.4. Other classification algorithms 179 10.3. Modeling Airbnb prices with classification algorithms 183 10.3.1. The work that’s already been done: overview 184 10.3.2. Models based on trees: decision tree versus Random Forest 185 10.3.3. Price prediction with kNN 190 10.4. Conclusion 193 Chapter 11. Cluster Analysis 195 11.1. Introduction 195 11.2. Cluster analysis: general framework 196 11.2.1. Cluster analysis applications 197 11.2.2. The clustering algorithm and the similarity measure 198 11.3. Grouping similar objects using k-means 200 11.3.1. The k-means algorithm 201 11.3.2. Determine the number of clusters 203 11.4. Hierarchical classification 205 11.4.1. The hierarchical model approach 206 11.4.2. Dendrograms 207 11.5. Discovering hidden structures with clustering algorithms 208 11.5.1. Illustration of the classification of prices based on different characteristics using the k-means algorithm 209 11.5.2. Identify the number of clusters k 210 11.6. Conclusion 213 Conclusion 215 References 217 Index 233
£125.06
ISTE Ltd and John Wiley & Sons Inc Perceptions and Analysis of Digital Risks
Book SynopsisThe concept of digital risk, which has become ubiquitous in the media, sustains a number of myths and beliefs about the digital world. This book explores the opposite view of these ideologies by focusing on digital risks as perceived by actors in their respective contexts.Perceptions and Analysis of Digital Risks identifies the different types of risks that concern actors and actually impact their daily lives, within education or various socio-professional environments. It provides an analysis of the strategies used by the latter to deal with these risks as they conduct their activities; thus making it possible to characterize the digital cultures and, more broadly, the informational cultures at work.This book offers many avenues for action in terms of educating the younger generations, training teachers and leaders, and mediating risks.Table of ContentsForeword xiFranc MORANDI Introduction xviiCamille CAPELLE Part 1. Risk Perceptions, Education and Learning 1 Chapter 1. Digital Risks: An Obstacle or a Lever for Education? 3Camille CAPELLE 1.1. Introduction 3 1.2. Digital risks and education: what are we talking about? 4 1.2.1. Digital risks 4 1.2.2. What are the risks in education? 8 1.3. Questioning perceptions of digital risks among new teachers 9 1.3.1. Why was this target audience chosen? 9 1.3.2. Methodology and data collection 10 1.4. Teachers’ perceptions of digital risks 11 1.4.1. When perceptions of risk inhibit any practice 11 1.4.2. When perceptions of risk freeze practices 14 1.4.3. When risk perceptions lead us to consider them in order to overcome them 18 1.5. Reflection on the role of digital risk representations in education 21 1.6. Conclusion 24 1.7. References 25 Chapter 2. Teenagers Faced with “Fake News”: Perceptions and the Evaluation of an Epistemic Risk 27Gilles SAHUT and Sylvie FRANCISCO 2.1. Introduction 27 2.2. Fake news: From production to reception 28 2.2.1. Characterizing the fake news phenomenon 29 2.2.2. The potential risks associated with fake news 31 2.2.3. The credibility of fake news 32 2.3. Methodological framework of the study 34 2.4. Results of the study 36 2.4.1. A heterogeneous understanding of the concept 37 2.4.2. A blurred perception of the goals of fake news 39 2.4.3. The diversity of fake news sources 40 2.4.4. Identifying fake news: heuristic processing and analytical strategies 42 2.4.5. A remote and controlled phenomenon? 45 2.5. Discussion of the results and reflections on media and information literacy 46 2.6. Conclusion 49 2.7. References 50 Chapter 3. “A Big Nebula that is a Bit Scary” (Louise, Trainee Schoolteacher): Training through/in Digital Technology, in School and in Professional Training 55Anne CORDIER 3.1. Social beings, above all else 57 3.1.1. A “fluid identity” to be grasped 57 3.1.2. Digital technology in the actors’ personal ecosystem 61 3.2. Understanding of digital technology in the classroom 62 3.2.1. Crystallization and awareness of issues 62 3.2.2. When the socio-technical framework hinders the entry of digital technology into the classroom 64 3.2.3. Rather modest and low-risk experiments 66 3.3. Teaching with and through digital technology: Constant risks 68 3.3.1. Tensions in the classroom 68 3.3.2. Tensions in training 71 3.3.3. Desires on both sides 73 3.4. Potential courses of action 76 3.5. References 78 Part 2. Risks in the Light of Socio-Economic Issues 81 Chapter 4. Top Managers Confronted with Information Risks: An Exploratory Study within the Telecommunications Sector 83Dijana LEKIC, Anna LEZON-RIVIÈRE and Madjid IHADJADENE 4.1. Introduction 83 4.2. Information risk: The conceptual field 84 4.3. Controlling information risks: Security policy 89 4.4. Information risk and management 91 4.5. Study methodology and the stakeholder group 93 4.6. Information risk: The perspective of top telecoms managers 94 4.6.1. Top managers as responsible for information risk management 94 4.6.2. Information risk management 97 4.6.3. Operational challenges related to the information risk management approach 100 4.7. Conclusion 104 4.8 Acknowledgments 106 4.9. References 106 Chapter 5. Cell Phones and Scamming Risks in Cameroon: Users’ Experiences and Socio-Institutional Responses 111Freddy TSOPFACK FOFACK and Abdel Bernazi RENGOU 5.1. Introduction 111 5.2. Mechanisms behind cell phone scamming in Cameroon: Exhibiting credulity 115 5.2.1. Setting the scene 116 5.2.2. Enticing but misleading proposals 117 5.2.3. Disguised telephone number confusion 119 5.3. The dynamics of cell phone use in Cameroon 121 5.3.1. The Ministry of Posts and Telecommunications 121 5.3.2. Agence Nationale des Technologies de l’Information et de la Communication 122 5.3.3. Agence de Régulation des Télécommunications 122 5.3.4. Cell phone operators 123 5.3.5. The judicial system and cell phone scams 124 5.3.6. Cell phone users and consumer associations 125 5.4. Socio-institutional governance of cell phone use in Cameroon: Optimal or approximate mediations? 126 5.4.1. Information deficit of the users 126 5.4.2. Insufficient means of action 127 5.4.3. Mis-selling of SIM cards by mobile operators: An “ingredient” of mobile scammers 128 5.4.4. The ease of monetary transactions 129 5.4.5. Technological constraints and border porosity 129 5.5. Conclusion 130 5.6. References 131 Part 3. Digital Risks: Practices and Mediation 135 Chapter 6. Towards a Normative Prescription of Information Practices on Digital Social Networks: A Study of Documentary Pedagogical Projects in Middle School 137Adeline ENTRAYGUES 6.1. Introduction 137 6.2. Contextualization of risk 138 6.3. Issues to consider 138 6.4. Research objects 139 6.5. Research protocol 142 6.6. Risk regarding DSNs in the pedagogical approach 144 6.6.1. Raising awareness of risks: An obvious approach for teacher librarians 144 6.6.2. Considering the views of learners and teachers 145 6.6.3. Considering the risks: Learners aware of digital dangers 148 6.7. Discovering DSNs in a school context: Dealing with risks 151 6.7.1. Pedagogical projects on DSNs to prevent risks: Teachers’ perspectives 151 6.7.2. Overcoming risks: Learners’ perspectives 152 6.8. Perspectives for an information culture 153 6.8.1. Risks, standards and education 153 6.8.2. A culture of information in training 154 6.9. Conclusion 155 6.10. References 155 Chapter 7. MIL as a Tool for Teachers to Prevent Risk and Transmit Digital Culture 159Julie PASCAU 7.1. Studying digital technology in schools from the perspective of teachers’ representations 159 7.1.1. Why be interested in representations? 161 7.1.2. The social representation of digital risks through the analysis of institutional discourses 163 7.2. What do digital and media literacy evoke in teachers? 164 7.2.1. The weak presence of digital technology and MIL in elementary school 165 7.2.2. Risks in the representations of MIL among primary school teachers 166 7.2.3. A positive perception of the role of digital technology in the classroom 169 7.3. The contours of media and information literacy according to teachers 171 7.3.1. The objects of MIL from the discourse of primary school teachers 172 7.3.2. What does digital technology mean for teachers? 173 7.4. What does the requirement to transmit digital culture mean for teachers? 178 7.4.1. Digital culture: A very vague concept 178 7.4.2. What primary school teachers think digital literacy means 180 7.5. Conclusion 187 7.6. References 189 Conclusion 193Camille CAPELLE Postface 197Vincent LIQUÈTE List of Authors 201 Index 203
£124.15
Edward Elgar Publishing Ltd Digital Transformations: New Tools and Methods
Book SynopsisTechnology is not just limited to technology companies, it impacts sectors such as healthcare, agriculture, and security. In the last few decades, countries, too, have started developing technologies or integrating technologies into their systems. As a result, all countries, regardless of size, need to understand the management of engineering and technology concepts. Digital Transformations reviews fundamentals and applications through existing and emerging technologies all around the world.Big data availability and the emergence of new tools provide opportunities to detect the emergence of new technologies. Some of the major elements of such analyses include bibliometrics, patent analysis and social network analysis. The authors focus on these three tools and demonstrate their use through applications such as Blockchain, Artificial Intelligence, Robotics, 3D printing, Wireless Power, Autonomous and Electric Driving, and Smart Homes.Through the examination of cases based on emerging technologies, the book provides a spectrum of these recent applications and serves as a reference for professionals, researchers and students on fundamentals of technology utilization tools.Trade Review‘Dr. Tugrul Daim has championed another masterpiece with this manuscript. This book helps transform vital information from the academic world into a blueprint that can be used by government and industry commercial leaders. A true first of its kind, as there is no other manuscript available to help engineering and technology managers navigate through the challenges presented by Digital Transformations.’ -- Matthew L. Tompkins, TC Defense, US‘The authors introduce the use of statistical methods such as bibliometrics, patent analysis and network analysis to understand trends, connections and leadership in technology innovation and also to identify key issues. Real-world case studies explore an array of innovations in the medical, power, transportation and home appliance fields. This approach illustrates how the techniques are useful while telling the story of some of today’s pivotal innovations.’ -- Fred Gordon, Energy Trust of Oregon, USTable of ContentsContents: Introduction to Digital Transformations 1. Bibliometric-based analyses 2. Patent-based analyses 3. Network-based analyses 4. Integrated analyses 5. Conclusion References Index
£90.76
Business Expert Press Obtaining Value from Big Data for Service Systems, Volume I: Big Data Management
Book SynopsisVolume I of this two-volume series focuses on the role of big data in service delivery systems. It discusses the definition and orientation to big data, applications of it in service delivery systems, how to obtain results that can affect/enhance service delivery, and how to build an effective big data organization.This volume will assist readers in fitting big data analysis into their service-based organizations. It will also help readers understand how to improve the use of big data to enhance their service-oriented organizations.
£21.80
Business Expert Press Obtaining Value from Big Data for Service Systems, Volume II: Big Data Technology
Book SynopsisVolume II of this series discusses the technology used to implement a big data analysis capability within a service-oriented organization. It discusses the technical architecture necessary to implement a big data analysis capability, some issues and challenges in big data analysis and utilization that an organization will face, and how to capture value from it.It will help readers understand what technology is required for a basic capability and what the expected benefits are from establishing a big data capability within their organization.
£21.80
Springer Nature Switzerland AG Data Science for Economics and Finance:
Book SynopsisThis open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. Table of Contents
£33.24
Springer Nature Switzerland AG Data Analysis and Classification: Methods and
Book SynopsisThis volume gathers peer-reviewed contributions that address a wide range of recent developments in the methodology and applications of data analysis and classification tools in micro and macroeconomic problems. The papers were originally presented at the 29th Conference of the Section on Classification and Data Analysis of the Polish Statistical Association, SKAD 2020, held in Sopot, Poland, September 7–9, 2020. Providing a balance between methodological contributions and empirical papers, the book is divided into five parts focusing on methodology, finance, economics, social issues and applications dealing with COVID-19 data. It is aimed at a wide audience, including researchers at universities and research institutions, graduate and doctoral students, practitioners, data scientists and employees in public statistical institutions.Table of ContentsPart 1: Methodology Chapter 1 - Evaluation of Two-Step Spectral Clustering Algorithm for Large Untypical Data Sets (Andrzej Dudek) Chapter 2 - Determining the Number of Groups in Cluster Analysis Using Classical Indexes and Stability Measures – Comparison of Results (Dorota Rozmus)Chapter 3 - Identification of the Words Most Frequently Used by Different Generations of Twitter Users (Agata Majkowska, Kamila Migdał-Najman, Krzysztof Najman and Katarzyna Raca)Chapter 4 - Classification Algorithms Applications for Information Security on the Internet: a Review (Michał Bryś)Chapter 5 - Outlier Detection with the Use of Isolation Forests (Krzysztof Najman and Krystian Zieliński)Part 2: Application in FinanceChapter 6 - Propositions of Transformations of Asymmetrical Nominants into Stimulants on the Example of Chosen Financial Ratios ( Barbara Batóg and Katarzyna Wawrzyniak)Chapter 7 - Gini Regression in The Capital Investment Risk Assessment – Sensitivity Risk Measures in Portfolio Analysis (Grażyna Trzpiot).- Part 3: Application in EconomicsChapter 8 - Enterprise Dark Data (Katarzyna Raca)Chapter 9 - The Significance of Medical Science Issues in Research Papers Published in the Field of Economics (Urszula Cieraszewska, Monika Hamerska, Paweł Lula and Marcela Zembura)Chapter 10 - Application of Duration Analysis Methods in the Study of the Exit of a Real Estate Sale Offer from the Offer Database System (Ewa Putek-Szeląg, Anna Gdakowicz)Chapter 11 - Is Society Ready for Long-Term Investments? – Profiles of Electricity Users in Silesia (Sylwia Słupik and Joanna Trzęsiok) Chapter 12 - The Use of the Spatial Taxonomic Measure of Development to Assess the Tourist Attractiveness of Districts of the Lesser Poland Province(Jacek Wolak).- Part 4: Application in Social Issues Chapter 13 - Models of Competing Events in Assessing the Effects of the Transition of Unemployed People Between the States of Registration and De-registration (Beata Bieszk-Stolorz).- Chapter 14 - Direct Adjusted Survival Probabilities in the Analysis of Finding a Job by the Unemployed Depending on Their Individual Characteristics(Wioletta Grzenda)Chapter 15 - Europe 2020 Strategy – Objective Evaluation of Realization and Subjective Assessment by Seniors as Beneficiaries of Social Assumptions (Klaudia Przybysz, Agnieszka Stanimir and Marta Wasiak)Chapter 16 - Do Seniors Get to the Disco by Bike or in a Taxi? – Classification of Seniors According to Their Preferred Means of Transport (Joanna Kos-Łabędowicz and Joanna Trzęsiok)Part 5: Application with COVID-19 Data Chapter 17 - The Impact of the COVID-19 Pandemic on the Economies of European Countries in the Period January-September 2020 Based on Economic Indicators (Ewelina Nojszewska and Agata Sielska)Chapter 18 - Modelling the Risk of Foreign Divestment in the Visegrad Group Countries During the COVID-19 Pandemic (Marcin Salamaga) Chapter 19 - Analysis of COVID-19 Dynamics in EU Countries Using the Dynamic Time Warping Method and ARIMA Models (Joanna Landmesser).
£104.99
Springer Nature Switzerland AG Deep Learning in Data Analytics: Recent
Book SynopsisThis book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society.Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.Table of ContentsStudy on Discrete Action Sequences using Deep Emotional Intelligence.- A Novel Noise Removal Technique Influenced by Deep Convolutional Autoencoders on Mammograms.- A High Security Framework through Human Brain using Algo Mixture Model Deep Learning Algorithm.- Knowledge Framework for Deep Learning: Congenital Heart Disease.- Computing System and Machine Learning.- Automatic Image Segmentation by Ranking based SVM in Convolutional Neural Network on Diabetic Fundus Image.
£132.99
Springer Nature Switzerland AG Harnessing the Power of Analytics
Book SynopsisThis text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different historical data, including aviation data and concrete data, interpreting the predictive models, and highlighting the steps to deploy the models and the steps ahead. The book combines the conceptual perspective and a hands-on approach to predictive analytics using SAS VIYA, an analytic and data management platform. The authors use SAS VIYA to focus on analytics to solve problems, highlight how analytics is applied in the airline and business environment, and compare several different modeling techniques. They decipher complex algorithms to demonstrate how they can be applied and explained within improving decisions.Table of ContentsChapter 1. Introduction to Analytics and Data Science. Chapter 2. Data Types Structure & Data Preparation Process. Chapter 3. Data Exploration and Data Visualization. Chapter 4. Evaluating Predictive Performance. Chapter 5. Decision Trees & Ensemble. Chapter 6. Regression Models. Chapter 7. Neural Networks. Chapter 8. Model Deployment.
£71.24
Springer Nature Switzerland AG Recurrent Neural Networks: From Simple to Gated
Book SynopsisThis textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.Table of ContentsIntroduction1. Network Architectures2. Learning Processes3. Recurrent Neural Networks (RNN)4. Gated RNN: The Long Short-Term Memory (LSTM) RNN5. Gated RNN: The Gated Recurrent Unit (GRU) RNN6. Gated RNN: The Minimal Gated Unit (MGU) RNN
£42.74
Springer Nature Switzerland AG Advanced Analytics and Learning on Temporal Data:
Book SynopsisThis book constitutes the refereed proceedings of the 6th ECML PKDD Workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2021, held during September 13-17, 2021. The workshop was planned to take place in Bilbao, Spain, but was held virtually due to the COVID-19 pandemic. The 12 full papers presented in this book were carefully reviewed and selected from 21 submissions. They focus on the following topics: Temporal Data Clustering; Classification of Univariate and Multivariate Time Series; Multivariate Time Series Co-clustering; Efficient Event Detection; Modeling Temporal Dependencies; Advanced Forecasting and Prediction Models; Cluster-based Forecasting; Explanation Methods for Time Series Classification; Multimodal Meta-Learning for Time Series Regression; and Multivariate Time Series Anomaly Detection. Table of ContentsOral Presentation.- Ranking by Aggregating Referees: Evaluating the Informativeness of Explanation Methods for Time Series Classification.- State Space approximation of Gaussian Processes for time-series forecasting.- Fast Channel Selection for Scalable Multivariate Time Series Classification.- Temporal phenotyping for characterisation of hospital care pathways of COVID patients.- A New Multivariate Time Series Co-clustering Non-Parametric Model Applied to Driving-Assistance Systems Validation.- TRAMESINO: Trainable Memory System for Intelligent Optimization of Road Traffic Control.- Detection of critical events in renewable energy production time series.- Poster Presentation.- Multimodal Meta-Learning for Time Series Regression.- Cluster-based Forecasting for Intermittent and Non-intermittent Time Series.- State discovery and prediction from multivariate sensor data.- RevDet: Robust and Memory Efficient Event Detection and Tracking in Large News Feeds.- From Univariate to Multivariate Time Series Anomaly Detection with Non-Local Information.
£44.99
Springer Nature Switzerland AG Machine Learning for Text
Book SynopsisThis second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing. Particular importance is placed on deep learning methods. The chapters of this book span three broad categories:1. Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection. Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing. Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.Table of Contents1 An Introduction to Text Analytics.- 2 Text Preparation and Similarity Computation.- 3 Matrix Factorization and Topic Modeling.- 4 Text Clustering.- 5 Text Classification: Basic Models.- 6 Linear Models for Classification and Regression.- 7 Classifier Performance and Evaluation.- 8 Joint Text Mining with Heterogeneous Data.- 9 Information Retrieval and Search Engines.- 10 Language Modeling and Deep Learning.- 11 Attention Mechanisms and Transformers.- 12 Text Summarization.- 13 Information Extraction and Knowledge Graphs.- 14 Question Answering.- 15 Opinion Mining and Sentiment Analysis.- 16 Text Segmentation and Event Detection.
£47.49
Springer International Publishing AG Business Intelligence: 7th International
Book SynopsisThis book constitutes the proceedings of the 7th International Conference on Business Intelligence, CBI 2022, which took place in Khouribga, Morocco, during May 26-28, 2022. The 23 full papers included in this book were carefully reviewed and selected from a total of 68 submissions. They were organized in topical sections as follows: decision support and artificial intelligence; business intelligence and database; and optimization and dynamic programming.Table of ContentsDecision Support and Artificial Intelligence.- Optimization Focused On Parallel Fuzzy Deep Belief Neural Network For Opinion Mining.- A Convolutional Neural Networks-Based Approach For Potato Disease Classification.- Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolu-tional Neural Networks.- Decision Boundary to improve the sensitivity of deep neural networks models. - Facial Expression Recognition Using a Hybrid ViT-CNN Aggregator.- Machine Learning Approach to Automate Decision Support on Information System Attacks.- Deep Reinforcement Learning for Bitcoin Trading.- An exploration of student grade retention prediction using machine learning algorithms.- Deep Learning Model For Educational Recommender Systems.- Comparative Study of Deep Learning Models for detection and classification of intracranial hemorrhage.- Business Intelligence and Database.- Increasing Student Engagement in Lessons and Assessing MOOC Participants Through Artificial Intelligence. -Mining frequents itemset and association rules in diabetic dataset.- Automatic text summarization for Moroccan Arabic dialect using an artificial intelligence approach.- Automatic Change Detection based on the Independent Component Analysis and Fuzzy C-means Methods.- Sentiment analysis decision system for tracking climate change opinion in Twitter.- Analysis of Decision Tree Algorithms for Diabetes Prediction.- How far can Deep Learning improve Arabic Part of Speech Tagging.- Optimization and Dynamic programming.- Analysis of Several Algorithms for DOA Estimation in Two Different Communication Models by a Comparative Study.- A Novel hybrid Approach for improving the accuracy of the Supervised Link Prediction based on Graph Structure Features in Social Networks. - Intelligent system based on GAN model for decision support in brain Tumor segmentation.- Hospital room management for Covid-19 patients using Petri nets.- Dimensionality reduction of MI-EEG data via convolutional autoencoders with a low size dataset.- Car tracking technique for DLES Project.
£58.49
Springer International Publishing AG Document Analysis Systems: 15th IAPR
Book SynopsisThis book constitutes the refereed proceedings of the 15th IAPR International Workshop on Document Analysis Systems, DAS 2022, held in La Rochelle, France, in May 2022.The full papers presented were carefully reviewed and selected from numerous submissions addressing key techniques of document analysis.
£89.99
Springer International Publishing AG Database Systems for Advanced Applications.
Book SynopsisThis volume constitutes the papers of several workshops which were held in conjunction with the 27th International Conference on Database Systems for Advanced Applications, DASFAA 2022, held as virtual event in April 2022. The 30 revised full papers presented in this book were carefully reviewed and selected from 65 submissions. DASFAA 2022 presents the following five workshops: · First workshop on Pattern mining and Machine learning in Big complex Databases (PMBD 2021) · 6th International Workshop on Graph Data Management and Analysis (GDMA 2022) · First International Workshop on Blockchain Technologies (IWBT2022) · 8th International Workshop on Big Data Management and Service (BDMS 2022) · First workshop on Managing Air Quality Through Data Science · 7th International Workshop on Big Data Quality Management (BDQM 2022). Table of ContentsAn Algorithm for Mining Fixed-Length High Utility Itemsets.- A Novel Method to Create Synthetic Samples with Autoencoder Multi-layer Extreme Learning Machine.- Pattern Mining: Current Challenges and Opportunities.- Why not to Trust Big Data: Identifying Existence of Simpson’s Paradox Localized Metric Learning for Large Multi-Class Extremely Imbalanced Face Database.- Top-k dominating queries on incremental datasets.- Collaborative Blockchain based Distributed Denial of Service Attack Mitigation approach with IP Reputation System.- Model-Driven Development of Distributed Ledger Applications Towards a Blockchain Solution for Customs Duty-Related Fraud.- Securing Cookies/Sessions through Non-Fungible Tokens.- Chinese Spelling Error Detection and Correction Based on Knowledge Graph Construction and Application of Event Logic Graph: A Survey.- Enhancing Low-resource Languages Question Answering with Syntactic Graph.- Profile Consistency Discrimination.- H-V：An Improved Coding Layout based on Erasure Coded Storage System.- Astral: An Autoencoder-based Model for Pedestrian Trajectory Prediction of Variable-Length.- A Survey on Spatiotemporal Data Processing Techniques in Smart Urban Rail.- Fast Vehicle Track Counting in Traffic Video.- Summary A Traffic Summarization System using Semantic Words.- Attention_Cooperated_Reinforcement_Learning_for_Multi_agent_Path_Planning.- Big Data-driven Stable Task Allocation in Ride-hailing Services.- Weighted_Mean_Field_Multi_Agent_Reinforcement_Learning_via_Reward_Attribution_Decomposition.- Evaluating Presto and SparkSQL with TPC-DS.- Optimizing the Age of Sensed Information in Cyber-Physical Systems.- Aggregate Query Result Correctness using pattern Tables.- Time Series Data Quality Enhancing based on pattern Alignment.- Research on Feature extraction method of data quality intelligent detection.- Big Data Resources to Support Research Opportunities on Air Pollution Analysis in India.- Air Quality Data Collection in Hyderabad Using Low-cost Sensors: Initial Experiences.- Visualizing Spatio-Temporal Variation of Ambient Air Pollution in Four Small Towns in India.
£66.49
Springer International Publishing AG Elements of Data Science, Machine Learning, and
Book SynopsisThe textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science: computer science; mathematics and statistics; and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, Q&A and examples.Table of Contents1. Introduction2. Introduction to learning from data3. Part 1: General topics4. Prediction models5. Error measures6. Resampling7. Data types8. Part 2: Core methods9. Maximum Likelihood & Bayesian analysis10. Clustering11. Dimension Reduction12. Classification13. Hypothesis testing14. Linear Regression15. Model Selection16. Part 3: Advanced topics17. Regularization18. Deep neural networks19. Multiple hypothesis testing20. Survival analysis21. Generalization error22. Theoretical foundations23. Conclusion.
£52.24
Springer International Publishing AG Intelligent Information and Database Systems:
Book SynopsisThis book constitutes the refereed proceedings of the 14th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2022, held Ho Chi Minh City, Vietnam in November 2022.The 113 full papers accepted for publication in these proceedings were carefully reviewed and selected from 406 submissions. The papers of the 2 volume-set are organized in the following topical sections: data mining and machine learning methods, advanced data mining techniques and applications, intelligent and contextual systems, natural language processing, network systems and applications, computational imaging and vision, decision support and control systems, and data modeling and processing for industry 4.0. The accepted and presented papers focus on new trends and challenges facing the intelligent information and database systems community.Table of ContentsAdvanced Data Mining Techniques and Applications.- Decision Support and Control Systems.- Deep Learning Models.- Internet of Things and Sensor Networks.- Natural Language Processing.- Social Networks and Recommender Systems.- Machine Learning and Data Mining.- Computer Vision Techniques.- Innovations in Intelligent Systems.
£999.99
Springer International Publishing AG Neural Information Processing: 29th International
Book SynopsisThe three-volume set LNCS 13623, 13624, and 13625 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22–26, 2022.The 146 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements.Table of ContentsTheory and Algorithms.- Solving Partial Differential Equations using Point-based Neural Networks.- Patch Mix Augmentation with Dual Encoders for Meta-Learning.- Tacit Commitments Emergence in Multi-agent Reinforcement Learning.- Saccade Direction Information Channel.- Shared-Attribute Multi-Graph Clustering with Global Self-Attention.- Mutual Diverse-Label Adversarial Training.- Multi-Agent Hyper-Attention Policy Optimization.- Filter Pruning via Similarity Clustering for Deep Convolutional Neural Networks.- FPD: Feature Pyramid Knowledge Distillation.- An effective ensemble model related to incremental learning in neural machine translation.- Local-Global Semantic Fusion Single-shot Classification Method.- Self-Reinforcing Feedback Domain Adaptation Channel.- General Algorithm for Learning from Grouped Uncoupled Data and Pairwise Comparison Data.- Additional Learning for Joint Probability Distribution Matching in BiGAN.- Multi-View Self-Attention for Regression Domain Adaptation with Feature Selection.- EigenGRF: Layer-Wise Eigen-Learning for Controllable Generative Radiance Fields.- Partial Label learning with Gradually Induced Error-Correction Output Codes.- HMC-PSO: A Hamiltonian Monte Carlo and Particle Swarm Optimization-based optimizer.- Heterogeneous Graph Representation for Knowledge Tracing.- Intuitionistic fuzzy universum support vector machine.- Support vector machine based models with sparse auto-encoder based features for classification problem.- Selectively increasing the diversity of GAN-generated samples.- Cooperation and Competition: Flocking with Evolutionary Multi-Agent Reinforcement Learning.- Differentiable Causal Discovery Under Heteroscedastic Noise.- IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels.- Adaptive Scaling for U-Net in Time Series Classification.- Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples.- SSPR: A Skyline-Based Semantic Place Retrieval Method.- Double Regularization-based RVFL and edRVFL Networks for Sparse-Dataset Classification.- Adaptive Tabu Dropout for Regularization of Deep Neural Networks.- Class-Incremental Learning with Multiscale Distillation for Weakly Supervised Temporal Action Localization.- Nearest Neighbor Classifier with Margin Penalty for Active Learning.- Factual Error Correction in Summarization with Retriever-Reader Pipeline.- Context-adapted Multi-policy Ensemble Method for Generalization in Reinforcement Learning.- Self-attention based multi-scale graph convolutional networks.- Synesthesia Transformer with Contrastive Multimodal Learning.- Context-based Point Generation Network for Point Cloud Completion.- Temporal Neighborhood Change Centrality for Important Node Identification in Temporal Networks.- DOM2R-Graph: A Web Attribute Extraction Architecture with Relation-aware Heterogeneous Graph Transformer.- Sparse Linear Capsules for Matrix Factorization-based Collaborative Filtering.- PromptFusion: a Low-cost Prompt-based Task Composition for Multi-task Learning.- A fast and efficient algorithm for filtering the training dataset.- Entropy-minimization Mean Teacher for Source-Free Domain Adaptive Object Detection.- IA-CL: A Deep Bidirectional Competitive Learning Method for Traveling Salesman Problem.- Boosting Graph Convolutional Networks With Semi-Supervised Training.- Auxiliary Network: Scalable and agile online learning for dynamic system with inconsistently available inputs.- VAAC: V-value Attention Actor-Critic for Cooperative Multi-agent Reinforcement Learning.- An Analytical Estimation of Spiking Neural Networks Energy Efficiency.- Correlation Based Semantic Transfer with Application to Domain Adaptation.- Minimum Variance Embedded Intuitionistic Fuzzy Weighted Random Vector Functional Link Network.- Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction.
£75.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£62.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£107.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.Table of ContentsBig data.- Toward Explainable Recommendation Via Counterfactual Reasoning.- Online Volume Optimization for Notifications via Long Short-Term Value Modeling.- Discovering Geo-referenced Frequent Patterns in Uncertain Geo-referenced Transactional Databases.- Financial data.- Joint Latent Topic Discovery and Expectation Modeling for Financial Markets.- Let the model make financial senses: a Text2Text generative approach for financial complaint identification.- Information retrieval and search.- Web-scale Semantic Product Search With Large Language Models.- Multi-task learning based Keywords weighted Siamese Model for semantic retrieval.- Relation-Aware Network with Attention-Based Loss for Few-Shot Knowledge Graph Completion.- MFBE: Leveraging Multi-Field Information of FAQs for Efficient Dense Retrieval.- Isotropic Representation Can Improve Dense Retrieval.- Knowledge-Enhanced Prototypical Network with Structural Semantics for Few-Shot Relation Classification.- Internet of Things.- MIDFA : Memory-Based Instance Division and Feature Aggregation Network for Video Object Detection.- Medical and biological data.- Vision Transformers for Small Histological Datasets learned through Knowledge Distillation.- Cascaded Latent Diffusion Models for High-Resolution Chest X-ray Synthesis.- DKFM: Dual Knowledge-guided Fusion Model for Drug Recommendation.- Hierarchical Graph Neural Network for Patient Treatment Preference Prediction with External Knowledge.- Multimedia and multimodal data.- An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition Performance.- Dynamically-Scaled Deep Canonical Correlation Analysis.- TCR: Short Video Title Generation and Cover Selection with Attention Refinement.- ItrievalKD: An Iterative Retrieval Framework Assisted with Knowledge Distillation for Noisy Text-to-Image Retrieval.- Recommender systems.- Semantic Relation Transfer for Non-overlapped Cross-domain Recommendations.- Interest Driven Graph Structure Learning for Session-Based Recommendation.- Multi-behavior Guided Temporal Graph Attention Network for Recommendation.- Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation.- Meta-learning Enhanced Next POI Recommendation by Leveraging Check-ins from Auxiliary Cities.- Global-Aware External Attention Deep Model for Sequential Recommendation.- Aggregately Diversified Bundle Recommendation via Popularity Debiasing and Configuration-aware Reranking.- Diversely Regularized Matrix Factorization for Accurate and Aggregately Diversified Recommendation.- kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval.- Staying or Leaving: A Knowledge-Enhanced User Simulator for Reinforcement Learning Based Short Video Recommendation.- RLMixer: A Reinforcement Learning Approach For Integrated Ranking With Contrastive User Preference Modeling.
£98.99
Springer International Publishing AG Advances in Knowledge Discovery and Data Mining:
Book SynopsisThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25–28, 2023.The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations.
£49.49
Springer International Publishing AG Formal Concept Analysis: 17th International
Book SynopsisThis book constitutes the proceedings of the 17th International Conference on Formal Concept Analysis, ICFCA 2023, which took place in Kassel, Germany, in July 2023.The 13 full papers presented in this volume were carefully reviewed and selected from 19 submissions. The International Conference on Formal Concept Analysis serves as a platform for researchers from FCA and related disciplines to showcase and exchange their research findings. The papers are organized in two topical sections, first "Theory" and second "Applications and Visualization".Table of ContentsTheory: Approximating fuzzy relation equations through concept lattices.- Doubly-Lexical Order Supports Standardisation and Recursive Partitioning of Formal Context.- Graph-FCA Meets Pattern Structures.- On the commutative diagrams among Galois connections involved in closure structures.- Scaling Dimension.- Three Views on Dependency Covers from an FCA Perspective.- A Triadic Generalisation of the Boolean Concept Lattice.- Applications and Visualization: Computing witnesses for centralising monoids on a three-element set.- Description Quivers for Compact Representation of Concept Lattices and Ensembles of Decision Trees.- Examples of clique closure systems.- On the maximal independence polynomial of the covering graph of the hypercube up to n=6.- Relational Concept Analysis in Practice: Capitalizing on Data Modeling using Design Patterns.- Representing Concept Lattices with Euler Diagrams.
£42.74
Springer International Publishing AG Hypothesis Generation and Interpretation: Design
Book SynopsisThis book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques. The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on “social infrastructure” applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases. The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.Table of ContentsBasic Concept.- Hypothesis.- Science and Hypothesis.- Regression.- Machine Learning and Integrated Approach.- Hypothesis Generation by Difference.- Methods for Integrated Hypothesis Generation.- Interpretation.
£143.99
Springer International Publishing AG Intelligent Systems: 12th Brazilian Conference,
Book SynopsisThe three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows:Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis;Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. Table of ContentsEmbracing data irregularities in multivariate time series with Recurrent and Graph Neural Networks.- Regulation and Ethics of Facial Recognition Systems: an analysis of cases in the Court of Appeal in the State of São Paulo.- A combinatorial optimization model and polynomial time heuristic for a problem of finding specific structural patterns in networks.- Efficient Density-Based Models for Multiple Machine Learning Solutions over Large Datasets.- Exploring Text Decoding Methods for Portuguese Legal Text Generation.- Community Detection for Multi-Label Classification.- A Monte Carlo Algorithm for Time-Constrained General Game Playing.- Alpha-MCMP: trade-offs between probability and cost in SSPs with the MCMP criterion.- Specifying Preferences over Policies using Branching Time Temporal Logic.- Logic-based Explanations for Linear Support Vector Classifiers with Reject Option.- The Multi-Attribute Fairer Cover Problem.- A Custom Bio-Inspired Algorithm for the Molecular Distance Geometry Problem.- Allocating Dynamic and Finite Resources to a Set of Known Tasks.- A Multi-algorithm approach to the optimization of thermal power plants operation.- An incremental MaxSAT-based model to learn interpretable and balanced classification rules.- d-CC Integrals: generalizing CC-integrals by restricted dissimilarity functions with applications to fuzzy-rule based systems.- FeatGeNN: Improving Model Performance for Tabular Data with Correlation-based Feature Extraction.- Hierarchical Time-aware Approach for Video Summarization.- Analyzing college student dropout risk prediction in real data using walk-forward validation.- The artificial intelligence as a technological resource in the application of tasks for the development of joint attention in children with autism.- Machine teaching: an explainable machine learning model for individualized education.- BLUEX: a benchmark based on Brazilian Leading Universities Entrance eXams.- Towards Generating P-Contrastive Explanations for Goal Selection in extended-BDI Agents.- Applying Theory of Mind to Multi-Agent Systems: A Systematic Literature Review.- A Spin-off Version of Jason for IoT and Embedded Multiagent Systems.- Hybrid Multilevel Explanation: A new approach for explaining regression models.- Explainability of COVID-19 Classification Models using Dimensionality Reduction of SHAP values.- An explainable model to support the decision about the appropriate therapy protocol for AML.- Bayes and Laplace versus the world: A new label attack approach in federated environments based on Bayesian Neural Networks.- MAT-Tree: A Tree-based Method for Multiple Aspect Trajectory Clustering.
£61.74
Springer International Publishing AG Intelligent Systems: 12th Brazilian Conference,
Book SynopsisThe three-volume set LNAI 14195, 14196, and 14197 constitutes the refereed proceedings of the 12th Brazilian Conference on Intelligent Systems, BRACIS 2023, which took place in Belo Horizonte, Brazil, in September 2023. The 90 full papers included in the proceedings were carefully reviewed and selected from 242 submissions. They have been organized in topical sections as follows:Part I: Best papers; resource allocation and planning; rules and feature extraction; AI and education; agent systems; explainability; AI models; Part II: Transformer applications; convolutional neural networks; deep learning applications; reinforcement learning and GAN; classification; machine learning analysis;Part III: Evolutionary algorithms; optimization strategies; computer vision; language and models; graph neural networks; pattern recognition; AI applications. Table of ContentsTransformer Model for Fault Detection From Brazilian Pre-Salt Seismic Data.- Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders.- Dog Face Recognition Using Vision Transformer.- Convolutional neural networks for the molecular detection of Covid-19.- Hierarchical Graph Convolutional Networks for Image Classification.- Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach.- Enhancing Stock Market Predictions through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-Market Analysis.- Ensemble architectures and efficient fusion techniques for Convolutional Neural Networks: an analysis on resource optimization strategies.- Dog Face Recognition using Deep Feature Embeddings.- Clinical oncology textual notes analysis using machine learning and deep learning.- EfficientDeepLab For Automated Trachea Segmentation On Medical Images.- Multi-Label Classification of Pathologies in Chest Radiograph Images Using DenseNet.- Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers.- Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence.- Deep Reinforcement Learning for Voltage Control in Power Systems.- Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging.- Occluded Face In-painting Using Generative Adversarial Networks - A ReviewClassification of facial images to assist in the diagnosis of Autism Spectrum Disorder: a study on the effect of face detection and landmark identification algorithms.- Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design.- AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification.- Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset.- Feature Selection and Hyperparameter Fine-tuning in Artificial Neural Networks for Wood Quality Classification.- A Feature-based Out-of-Distribution Detection Approach in Skin Lesion Classification.- A framework for characterizing what makes an instance hard to classify.- Physicochemical Properties for Promoter Classification.- Critical analysis of AI indicators in terms of weighting and aggregation approaches.- Estimating Code Running Time Complexity with Machine LearningThe Effect of Statistical Hypothesis Testing on Machine Learning Model Selection.
£61.74
