Data warehousing Books
Pearson Education (US) Foundational Python for Data Science
Book Synopsis Kennedy Behrman is a veteran software and data engineer. He first used Python writing asset management systems in the Visual Effects industry. He then moved into the startup world, using Python at startups using machine learning to characterize videos and predict the social media power of athletes. Table of ContentsPreface xiii I: Learning Python in a Notebook Environment 1 1 Introduction to Notebooks 3 2 Fundamentals of Python 13 3 Sequences 25 4 Other Data Structures 37 5 Execution Control 55 6 Functions 67 II: Data Science Libraries 83 7 NumPy 85 8 SciPy 103 9 Pandas 113 10 Visualization Libraries 135 11 Machine Learning Libraries 153 12 Natural Language Toolkit 159 III: Intermediate Python 171 13 Functional Programming 173 14 Object-Oriented Programming 187 15 Other Topics 201 A Answers to End-of-Chapter Questions 215 Index 221
£42.74
Pearson Education (US) Big Data Fundamentals
Book SynopsisThomas Erl is a top-selling IT author, founder of Arcitura Education and series editor of the Prentice Hall Service Technology Series from Thomas Erl. With more than 200,000 copies in print worldwide, his books have become international bestsellers and have been formally endorsed by senior members of major IT organizations, such as IBM, Microsoft, Oracle, Intel, Accenture, IEEE, HL7, MITRE, SAP, CISCO, HP and many others. As CEO of Arcitura Education Inc., Thomas has led the development of curricula for the internationally recognized Big Data Science Certified Professional (BDSCP), Cloud Certified Professional (CCP) and SOA Certified Professional (SOACP) accreditation programs, which have established a series of formal, vendor-neutral industry certifications obtained by thousands of IT professionals around the world. Thomas has toured more than 20 countries as a speaker and instructor. More than 100 articles and interviews by Thomas have been published in numerous publicaTable of ContentsAcknowledgments xviiReader Services xviiiPART I: THE FUNDAMENTALS OF BIG DATAChapter 1: Understanding Big Data 3 Concepts and Terminology 5 Datasets 5 Data Analysis 6 Data Analytics 6 Descriptive Analytics 8 Diagnostic Analytics 9 Predictive Analytics 10 Prescriptive Analytics 11 Business Intelligence (BI) 12 Key Performance Indicators (KPI) 12 Big Data Characteristics 13 Volume 14 Velocity 14 Variety 15 Veracity 16 Value 16 Different Types of Data 17 Structured Data 18 Unstructured Data 19 Semi-structured Data 19 Metadata 20 Case Study Background 20 History 20 Technical Infrastructure and Automation Environment 21 Business Goals and Obstacles 22 Case Study Example 24 Identifying Data Characteristics 26 Volume 26 Velocity 26 Variety 26 Veracity 26 Value 27 Identifying Types of Data 27 Chapter 2: Business Motivations and Drivers for Big Data Adoption 29 Marketplace Dynamics 30 Business Architecture 33 Business Process Management 36 Information and Communications Technology 37 Data Analytics and Data Science 37 Digitization 38 Affordable Technology and Commodity Hardware 38 Social Media 39 Hyper-Connected Communities and Devices 40 Cloud Computing 40 Internet of Everything (IoE) 42 Case Study Example 43 Chapter 3: Big Data Adoption and Planning Considerations 47 Organization Prerequisites 49 Data Procurement 49 Privacy 49 Security 50 Provenance 51 Limited Realtime Support 52 Distinct Performance Challenges 53 Distinct Governance Requirements 53 Distinct Methodology 53 Clouds 54 Big Data Analytics Lifecycle 55 Business Case Evaluation 56 Data Identification 57 Data Acquisition and Filtering 58 Data Extraction 60 Data Validation and Cleansing 62 Data Aggregation and Representation 64 Data Analysis 66 Data Visualization 68 Utilization of Analysis Results 69 Case Study Example 71 Big Data Analytics Lifecycle 73 Business Case Evaluation 73 Data Identification 74 Data Acquisition and Filtering 74 Data Extraction 74 Data Validation and Cleansing 75 Data Aggregation and Representation 75 Data Analysis 75 Data Visualization 76 Utilization of Analysis Results 76 Chapter 4: Enterprise Technologies and Big Data Business Intelligence 77 Online Transaction Processing (OLTP) 78 Online Analytical Processing (OLAP) 79 Extract Transform Load (ETL) 79 Data Warehouses 80 Data Marts 81 Traditional BI 82 Ad-hoc Reports 82 Dashboards 82 Big Data BI 84 Traditional Data Visualization 84 Data Visualization for Big Data 85 Case Study Example 86 Enterprise Technology 86 Big Data Business Intelligence 87 PART II: STORING AND ANALYZING BIG DATAChapter 5: Big Data Storage Concepts 91 Clusters 93 File Systems and Distributed File Systems 93 NoSQL 94 Sharding 95 Replication 97 Master-Slave 98 Peer-to-Peer 100 Sharding and Replication 103 Combining Sharding and Master-Slave Replication 104 Combining Sharding and Peer-to-Peer Replication 105 CAP Theorem 106 ACID 108 BASE 113 Case Study Example 117 Chapter 6: Big Data Processing Concepts 119 Parallel Data Processing 120 Distributed Data Processing 121 Hadoop 122 Processing Workloads 122 Batch 123 Transactional 123 Cluster 124 Processing in Batch Mode 125 Batch Processing with MapReduce 125 Map and Reduce Tasks 126 Map 127 Combine 127 Partition 129 Shuffle and Sort 130 Reduce 131 A Simple MapReduce Example 133 Understanding MapReduce Algorithms 134 Processing in Realtime Mode 137 Speed Consistency Volume (SCV) 137 Event Stream Processing 140 Complex Event Processing 141 Realtime Big Data Processing and SCV 141 Realtime Big Data Processing and MapReduce 142 Case Study Example 143 Processing Workloads 143 Processing in Batch Mode 143 Processing in Realtime 144 Chapter 7: Big Data Storage Technology 145 On-Disk Storage Devices 147 Distributed File Systems 147 RDBMS Databases 149 NoSQL Databases 152 Characteristics 152 Rationale 153 Types 154 Key-Value 156 Document 157 Column-Family 159 Graph 160 NewSQL Databases 163 In-Memory Storage Devices 163 In-Memory Data Grids 166 Read-through 170 Write-through 170 Write-behind 172 Refresh-ahead 172 In-Memory Databases 175 Case Study Example 179 Chapter 8: Big Data Analysis Techniques 181 Quantitative Analysis 183 Qualitative Analysis 184 Data Mining 184 Statistical Analysis 184 A/B Testing 185 Correlation 186 Regression 188 Machine Learning 190 Classification (Supervised Machine Learning) 190 Clustering (Unsupervised Machine Learning) 191 Outlier Detection 192 Filtering 193 Semantic Analysis 195 Natural Language Processing 195 Text Analytics 196 Sentiment Analysis 197 Visual Analysis 198 Heat Maps 198 Time Series Plots 200 Network Graphs 201 Spatial Data Mapping 202 Case Study Example 204 Correlation 204 Regression 204 Time Series Plot 205 Clustering 205 Classification 205 Appendix A: Case Study Conclusion 207About the Authors 211 Thomas Erl 211 Wajid Khattak 211 Paul Buhler 212 Index 213
£26.54
Manning Publications Data Pipelines with Apache Airflow Second Edition
a huge range and FREE tracked UK delivery on ALL orders.
£41.39
Pearson Education (US) Product Analytics
Book SynopsisJoanne Rodrigues is an experienced data scientist with master's degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behavior by using economic and sociological theory in the context of complex mathematical models.Table of Contents Part I: Qualitative Methodology Chapter 1: Data in Action: A Model of a Dinner Party Chapter 2: Building a Theory of the Universe–The Social Universe Chapter 3: The Coveted Goal Post: How to Change User Behavior Part II: Basic Statistical Methods Chapter 4: Distributions in User Analytics Chapter 5: Retained? Metric Creation and Interpretation Chapter 6: Why Are My Users Leaving? The Ins and Outs of A/B Testing Part III: Predictive Methods Chapter 7: Modeling the User Space: k-Means and PCA Chapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines Chapter 9: Forecasting Population Changes in Product: Demographic Projections Part IV: Causal Inference Methods Chapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design Chapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches Chapter 12: Developing Heuristics in Practice: Statistical Matching and Hill’s Causality Conditions Chapter 13: Uplift Modeling Part V: Basic, Predictive, and Causal Inference Methods in R Chapter 14: Metrics in R Chapter 15: A/B Testing, Predictive Modeling, and Population Projection in R Chapter 16: Regression Discontinuity, Matching, and Uplift in R Conclusion
£36.09
O'Reilly Media Data Pipelines Pocket Reference
Book SynopsisData pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack.
£20.39
O'Reilly Media Efficient MySQL Performance
Book SynopsisThis practical book bridges the gap by teaching software engineers mid-level MySQL knowledge beyond the fundamentals, but well shy of deep-level internals required by database administrators (DBAs).
£39.74
O'Reilly Media Fundamentals of Data Observability
Book Synopsis
£39.74
John Wiley & Sons Inc Kimballs Data Warehouse Toolkit Classics 3 Volume
Book Synopsis
£104.50
Elsevier Science Data Architecture A Primer for the Data Scientist
Book SynopsisTable of Contents1. An Introduction to Data Architecture2. The End-State Architecture - The "World Map"3. Transformations in the End-State Architecture4. A Brief History of Big Data5. The Siloed Application Environment6. Introduction to Data Vault 2.07. The Operational Environment: A Short History8. A Brief History of Data Architecture9. Repetitive Analytics: Some Basics10. Nonrepetitive Data11. Operational Analytics: Response Time12. Operational Analytics13. Personal Analytics14. Data Models Across the End-State Architecture15. The System of Record16. Business Value and the End-State Architecture17. Managing Text18. An Introduction to Data Visualizations
£999.99
APress Oracle Database Upgrade and Migration Methods
Book Synopsis Learn all of the available upgrade and migration methods in detail to move to Oracle Database version 12c. You will become familiar with database upgrade best practices to complete the upgrade in an effective manner and understand the Oracle Database 12c patching process. So it''s time to upgrade Oracle Database to version 12c and you need to choose the appropriate method while considering issues such as downtime. This book explains all of the available upgrade and migration methods so you can choose the one that suits your environment. You will be aware of the practical issues and proactive measures to take to upgrade successfully and reduce unexpected issues. With every release of Oracle Database there are new features and fixes to bugs identified in previous versions. As each release becomes obsolete, existing databases need to be upgraded. Oracle Database Upgrade and Migration Methods explains each method along Table of Contents PART I: packetC Background 1 CHAPTER 1: Getting Started 3 Introduction to Database upgrade Necessities of Database upgrade Benefits of Database upgrade Hurdles that affect Database upgrade decision Types of Database upgrade Things to consider before upgrade Engineers involved in upgrade activity Upgrade compatibility matrix Best practices of Database upgrade Database Migration Situations demand Migration Things to consider before migration summary PART II: Language Reference 53 n CHAPTER 2: Database Upgrade methods 55 DBUA Manual/Command line upgrade Export/Import Datapump Transportable Tablespace Golden Gate Create Table as Select (CTAS) Transient Logical Standby Full Transportable Tablespace Summary n CHAPTER 3: Comparison between upgrade methods 151 Comparison between methods 9 Real Application testing (RAT) 10 How to choose best Upgrade method 11 Summary n CHAPTER 4: Upgrade using Database backup 159 Cold backup Hot backup (User-Managed) Logical backup (expdp/impdp) RMAN backup (using duplicate option) Summary n CHAPTER 5: Database Migration methods 171 Export/Import Datapump Transportable Tablespace (TTS) Golden Gate Copy table as select (CTAS) Transport Database Heterogenous Standby database Oracle Streams Summary n CHAPTER 6: Migration of Oracle database from Non-ASM to ASM 175 Introduction Moving Datafiles Online from NON-ASM < to ASM Migrating Oracle 12c CDB with PDBs from NON ASM to ASM using EM Cloud Control 13c ............... Migrating Oracle 12c CDB with PDBs from NON ASM to ASM using RMAN Summary n CHAPTER 7: GI and DB upgrade in RAC environment 205 Introduction CVU Pre-Upgrade Check tool Execution Steps for ORAchk Rolling upgrade for Oracle GI Upgrading 11g RAC to 12c RAC using DBUA Upgrading 11g RAC to 12c RAC Manual Upgrading 11g RAC to 12c RAC using EM 13c Summary PART III: Developing Applications 215 n CHAPTER 8: Database upgrade in DG environment 217 Dummy Text Dummy Text Virtual Dummy Text Dummy Text Dummy Text Dummy Text Flow Dummy Text Summary n CHAPTER 9: Database upgrade in EBS environment 223 Prerequisite steps Preupgrade steps Upgrade steps Post upgrade steps Summary CHAPTER 10: Database upgrade in 12c Multitenant environment Migrate lower version database to Multitenant architecture Container database upgrade Pluggable database upgrade Summary n CHAPTER 11: Databases migrate in Multitenant environment 237 Pluggable database migrate Need for Migrate Migration steps Summary n CHAPTER 12: Oracle Database Patching Stratergies 245 Patching Introduction Opatch tool Types of patches Patch apply stratergies (online and offline patching).... PSU and SPU patching Patch apply in RAC and DG environment Datapatch Queryable patch inventory Summary n CHAPTER 13: Database Downgrade 263 Introduction Limitations of Oracle database downgrade Database downgrade steps Downgrade using database flashback Summary n CHAPTER 14: Database upgrade in 12.2 281 Preupgrade checks Upgrade Emulation DBUA Manual Database upgrade Pluggable database upgrade Downgrade 12.2 database to earlier version............... Summary n n APPENDIX A: Reference Tables 383 n APPENDIX B: Dummy Text 395 INDEX 433
£44.99
Springer International Publishing AG New Horizons for a Data-Driven Economy: A Roadmap
Book SynopsisIn this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe.This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. Trade Review“The book provides rich information on the different processes involved in big data value chain and explains each process with case studies in diverse industrial sectors. … the book can help academic researchers, undergraduate students, and graduate students because it contains information about big data and its recent development and generates some research ideas. This book can also facilitate government officials and executives of different organizations to consider future roadmap by taking advantage of big data.” (Sunny Sun and Rob Law, Information Technology & Tourism, Vol. 17, 2017)Table of Contents
£33.74
BPB Publications Querying Databricks with Spark SQL: Leverage SQL
Book Synopsis
£31.34
Manning Publications Graph Databases in Action
Book SynopsisGraph Databases in Action teaches readers everything they need to know to begin building and running applications powered by graph databases. Right off the bat, seasoned graph database experts introduce readers to just enough graph theory, the graph database ecosystem, and a variety of datastores. They also explore modelling basics in action with real-world examples, then go hands-on with querying, coding traversals, parsing results, and other essential tasks as readers build their own graph-backed social network app complete with a recommendation engine! Key Features · Graph database fundamentals · An overview of the graph database ecosystem · Relational vs. graph database modelling · Querying graphs using Gremlin · Real-world common graph use cases For readers with basic Java and application development skills building in RDBMS systems such as Oracle, SQL Server, MySQL, and Postgres. No experience with graph databases is required. About the technology Graph databases store interconnected data in a more natural form, making them superior tools for representing data with rich relationships. Unlike in relational database management systems (RDBMS), where a more rigid view of data connections results in the loss of valuable insights, in graph databases, data connections are first priority. Dave Bechberger has extensive experience using graph databases as a product architect and a consultant. He’s spent his career leveraging cutting-edge technologies to build software in complex data domains such as bioinformatics, oil and gas, and supply chain management. He’s an active member of the graph community and has presented on a wide variety of graph-related topics at national and international conferences. Josh Perryman is technologist with over two decades of diverse experience building and maintaining complex systems, including high performance computing (HPC) environments. Since 2014 he has focused on graph databases, especially in distributed or big data environments, and he regularly blogs and speaks at conferences about graph databases.
£37.99
Pearson Education Exam Ref DP600 Implementing Analytics Solutions
Book SynopsisDaniil Maslyuk is an independent business intelligence consultant, trainer, and speaker who specializes in Microsoft Power BI. Daniil blogs at xxlbi.com and tweets as @DMaslyuk. Johnny Winter is a data and analytics consultant who has been working with business intelligence software since 2007, specializing in the Microsoft data platform since 2016. He's a self-confessed business intelligence geek, and in his spare time runs the website and YouTube channel Greyskull Analytics, where he likes to nerd out about all things analytics. Štepán Rešl is a lead technical consultant and a Microsoft MVP in the Data Platform category. As a technical consultant, Štepán focuses on assisting medium and large organizations in deploying and maintaining their data solutions. He is also a speaker and co-organizer of conferences. In his spare time, he runs a blog called DataMeerkat, where he focuses on topics related to
£999.99
O'Reilly Media The Cloud Data Lake
Book SynopsisAuthor Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance.
£39.74
Springer Principles of Data Mining
Book SynopsisIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A Essential Mathematics.- Appendix B Datasets.- Appendix C Sources of Further Information.- Appendix D Glossary and Notation.- Appendix E SolutioTable of ContentsIntroduction to Data Mining.- Data for Data Mining.- Introduction to Classification: Naïve Bayes and Nearest Neighbour.- Using Decision Trees for Classification.- Decision Tree Induction: Using Entropy for Attribute Selection.- Decision Tree Induction: Using Frequency Tables for Attribute Selection.- Estimating the Predictive Accuracy of a Classifier.- Continuous Attributes.- Avoiding Overfitting of Decision Trees.- More About Entropy.- Inducing Modular Rules for Classification.- Measuring the Performance of a Classifier.- Dealing with Large Volumes of Data.- Ensemble Classification.- Comparing Classifiers.- Associate Rule Mining I.- Associate Rule Mining II.- Associate Rule Mining III.- Clustering.- Mining.- Classifying Streaming Data.- Classifying Streaming Data II: Time-dependent Data.- An Introduction to Neural Networks.- Appendix A – Essential Mathematics.- Appendix B – Datasets.- Appendix C – Sources of Further Information.- Appendix D – Glossary and Notation.- Appendix E – Solutions to Self-assessment Exercises.- Index.
£37.99
Springer Nature Switzerland AG Game Theory for Networks: 8th International EAI Conference, GameNets 2019, Paris, France, April 25–26, 2019, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 8th EAI International Conference on Game Theory for Networks, GameNets 2019, held in Paris, France, in April 2019. The 8 full and 3 short papers presented were carefully reviewed and selected from 17 submissions. They are organized in the following topical sections: Game Theory for Wireless Networks; Games for Economy and Resource Allocation; and Game Theory for Social Networks.Table of ContentsGame Theory for Wireless Networks.- Games for Economy and Resource Allocation.- Game Theory for Social Networks.
£37.99
Springer Nature Switzerland AG Digital Libraries: The Era of Big Data and Data Science: 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings
Book SynopsisThis book constitutes the thoroughly refereed proceedings of the 16th Italian Research Conference on Digital Libraries, IRCDL 2020, held in Bari, Italy, in January 2020.The 12 full papers and 6 short papers presented were carefully selected from 26 submissions. The papers are organized in topical sections on information retrieval, bid data and data science in DL; cultural heritage; open science. Table of ContentsInformation Retrieval.- Bid Data and Data Science in DL.- Cultural Heritage.- Open Science.
£53.99
Springer Nature Switzerland AG Information Retrieval: 27th China Conference, CCIR 2021, Dalian, China, October 29–31, 2021, Proceedings
Book SynopsisThis book constitutes the refereed proceedings of the 27th China Conference on Information Retrieval, CCIR 2021, held in Dalian, China, in October 2021.The 15 full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections: search and recommendation, NLP for IR, IR in Education, and IR in Biomedicine.Table of ContentsSearch and Recommendation.- NLP for IR.- IR in Education.- IR in Biomedicine.
£49.49
Springer International Publishing AG The Semantic Web: 19th International Conference, ESWC 2022, Hersonissos, Crete, Greece, May 29 – June 2, 2022, Proceedings
Book SynopsisChapters “No. 10 and No. 21” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Table of ContentsResearch.- Resources.- In-Use Track.
£999.99
Springer International Publishing AG The Semantic Web: ESWC 2022 Satellite Events: Hersonissos, Crete, Greece, May 29 – June 2, 2022, Proceedings
Book SynopsisThis book constitutes the proceedings of the satellite events held at the 19th Extended Semantic Web Conference, ESWC 2022, during May—June in Hersonissos, Greece, 2022. The included satellite events are: the poster and demo session; the PhD symposium; industry track; project networking; workshops and tutorials. During ESWC 2022, the following ten workshops took place:10th Linked Data in Architecture and Construction Workshop (LDAC 2022); 5th International Workshop on Geospatial Linked Data (GeoLD 2022); 5th Workshop on Semantic Web solutions for large-scale biomedical data analytics (SeMWeBMeDA 2022); 7th Natural Language Interfaces for the Web of Data (NLIWOD+QALD 2022); International Workshop on Knowledge Graph Generation from Text (Text2KG 2022); 3rd International Workshop on Deep Learning meets Ontologies and Natural Language Processing (DeepOntoNLP 2022); 1st Workshop on Modular Knowledge (ModularK 2022); Third International Workshop On Knowledge Graph Construction (KGCW 2022); Third International Workshop On Semantic Digital Twins (SeDIT 2022); and the 1st International Workshop on Semantic Industrial Information Modelling (SemIIM 2022). Table of Contents Summary of Workshops and Tutorials at European Semantic Web Conference 2022.- Posters and Demos.- Towards UML-style Visual Queries over Wikidata.- Using the ODRL Profile for Access Control for Solid Pod Resource Governance.- Relation Canonicalization in Open Knowledge Graphs: A Quantitative Analysis.- Harmonizing and Using Numismatic Linked Data in Digital Humanities Research and Application Development: Case DigiNUMA.- Extending AgreementMakerLight to Perform Holistic Ontology Matching.- It’s all in the Name: Entity Typing using Multilingual Language Models.- The Supervised Semantic Similarity Toolkit.- Tab2Onto: Unsupervised Semantification with Knowledge Graph Embeddings.- DataSpecer: A Model-Driven Approach to Managing Data Specifications.- Towards Query Processing over Heterogeneous Federations of RDF Data sources.- SAND: A Tool for Creating Semantic Descriptions of Tabular Sources.- BLAST: Block Applications for Things.- Leibniz Data Manager – A Research Data Management System.- Towards Knowledge Graph-Agnostic SPARQL Query Validation for Improving Question Answering.- Towards Generalized Welding Ontology in line with ISO and Knowledge Graph Construction.- O’FAIRe: Ontology FAIRness Evaluator in the AgroPortal semantic resource repository.- domOS Common Ontology: Web of Things Discovery in Smart Buildings.- WeaKG-MF: a Knowledge Graph of Observational Weather Data.- DAGOBAH UI: A New Hope For Semantic Table Interpretation.- KartoGraphI: Drawing a Map of Linked Data.- WikidataComplete – An easy-to-use method for rapid validation of text-extracted new facts applied to the Wikidata knowledge graph.- Query-based Industrial Analytics over Knowledge Graphs with Ontology Reshaping.- Semantic Video Entity Linking.- Walk this Way! Entity Walks and Property Walks for RDF2vec.- Self-Verifying Web Resource Representations using Solid, RDF-star and Signed URIs.- From OWL to Graphol: importing ontologies into Eddy the editor.- Audio Ontologies for Intangible Cultural Heritage.- Ontology Matching Through Absolute Orientation of Embedding Spaces.- Semantic modeling and reconstruction of drones’ Trajectories.- How to Search and Contextualize Scenes inside Videos for Enriched Watching Experience: Case Stories of the Second World War Veterans.- PhD Symposium.- (Semi-) Automatic construction of knowledge graph Metadata.- Towards a Similarity Algorithm for Controlled Vocabularies within the Digital Humanities.- Causal Domain Adaptation for Information Extraction from Complex Conversations.- Knowledge Graph Population with Out-of-KG Entities.- Dynamic Knowledge Graph Embeddings via Local Embedding Reconstructions.- Leveraging Standards in Model-Centric Geospatial Knowledge Graph Creation.- Building Narrative Structures from Knowledge Graphs.- Using Referential Language Games for Task-oriented Ontology Alignment.- Balancing RDF generation from heterogeneous data sources.- Geological Information Capture with Sketches and Ontologies.- Industry.- The Data Value Quest: A Holistic Semantic Approach at Bosch.- Extracting Subontologies from SNOMED CT.- “Semantify” business and content to meet demands for expert solutions in professional markets.- Enhancing Knowledge Graph Generation with Ontology Reshaping – Bosch Case.- Semantic Data Integration for Monitoring Operators’ Ergonomics in an Automotive Manufacturing Setting.- Semantic Description of Equipment and its Controls in Building Automation Systems.
£999.99
Springer International Publishing AG The Semantic Web: ESWC 2017 Satellite Events: ESWC 2017 Satellite Events, Portorož, Slovenia, May 28 – June 1, 2017, Revised Selected Papers
Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.Table of ContentsQuerying the Web of Data.- Semantic Web Solutions for Large-Scale Biomedical Data Analytics.- Scientometrics.- RDF Stream Processing.- Emotions, Modality, Sentiment Analysis and the Semantic Web.- Applications of Semantic Web Technologies in Robotics.- Linked Data and Distributed Ledgers.- Linked Data Quality.- Semantic Deep Learning.- Humanities in the Semantic Web.
£49.49
Springer International Publishing AG A Practical Guide to Sentiment Analysis
a huge range and FREE tracked UK delivery on ALL orders.
£116.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Ubiquitous Social Media Analysis: Third International Workshops MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers
Book SynopsisThis book constitutes the thoroughly refereed joint post-proceedings of the Third International Workshop on Mining Ubiquitous and Social Environments, MUSE 2012, held in Bristol, UK, in September 2012, and the Third International Workshop on Modeling Social Media, MSM 2012, held in Milwaukee, WI, USA, in June 2012. The 8 full papers included in the book are revised and significantly extended versions of papers submitted to the workshops. They cover a wide range of topics organized in three main themes: communities and group structure in ubiquitous social media; ubiquitous modeling and aspects of social interactions and influence.Table of ContentsHow to Carve up the World: Learning and Collaboration for Structure Recommendation.- A Topological Approach for Detecting Twitter Communities with Common Interests.- Using Geographic Cost Functions to Discover Vessel Itineraries from AIS Messages.- Social Media as a Source of Sensing to Study City Dynamics and Urban Social Behavior: Approaches, Models and Opportunities.- An Analysis of Interactions within and between Extreme Right Communities in Social Media.- Who will Interact with Whom? A Case-Study in Second Life Using Online Social Network and Location-Based Social Network Features to Predict Interactions between Users.- Identifying Influential Users by Their Postings in Social Networks.- Modeling a Web Forum Ecosystem into an Enriched Social Graph.
£41.99
BPB Publications Data Engineering with AWS
Book SynopsisBuild petabyte-scale data lakes using S3 and Lake Formation. Implement real-time streaming pipelines with Kinesis and Lambda. Design cost-optimized data warehouses using Amazon Redshift. Create modern data mesh architectures on AWS.
£999.99
Independently Published SQL Made Easy: Tips and Tricks to Mastering SQL
Book Synopsis
£13.17
BPB Publications Data Engineering Design Patterns
Book SynopsisKey data engineering patterns. Data ingestion and processing patterns. Modern architectures like Lambda. Explore time-tested data patterns of ETL and ELT. Modern data systems like data lake and medallion architectures.
£29.99
Pearson Education (US) Pandas for Everyone
Book SynopsisDaniel Chen is a graduate student in the Interdisciplinary PhD program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Polytechnic Institute and State University (Virginia Tech). He is involved with Software Carpentry as an instructor, Mentoring Committee Member, and currently serves as the Assessment Committee Chair. He completed his Masters in Public Health at Columbia University Mailman School of Public Health in Epidemiology with a certificate in Advanced Epidemiology and currently extending his Master's thesis work in the Social and Decision Analytics Laboratory under the Virginia Bioinformatics Institute on attitude diffusion in social networks.Table of ContentsForeword by Anne M. Brown xxiii Foreword by Jared Lander xxv Preface xxvii Changes in the Second Edition xxxix Part I: Introduction 1 Chapter 1. Pandas DataFrame Basics 3 Learning Objectives 3 1.1 Introduction 3 1.2 Load Your First Data Set 4 1.3 Look at Columns, Rows, and Cells 6 1.4 Grouped and Aggregated Calculations 23 1.5 Basic Plot 27 Conclusion 28 Chapter 2. Pandas Data Structures Basics 31 Learning Objectives 31 2.1 Create Your Own Data 31 2.2 The Series 33 2.3 The DataFrame 42 2.4 Making Changes to Series and DataFrames 45 2.5 Exporting and Importing Data 52 Conclusion 63 Chapter 3. Plotting Basics 65 Learning Objectives 65 3.1 Why Visualize Data? 65 3.2 Matplotlib Basics 66 3.3 Statistical Graphics Using matplotlib 72 3.4 Seaborn 78 3.5 Pandas Plotting Method 111 Conclusion 115 Chapter 4. Tidy Data 117 Learning Objectives 117 Note About This Chapter 117 4.1 Columns Contain Values, Not Variables 118 4.2 Columns Contain Multiple Variables 122 4.3 Variables in Both Rows and Columns 126 Conclusion 129 Chapter 5. Apply Functions 131 Learning Objectives 131 Note About This Chapter 131 5.1 Primer on Functions 131 5.2 Apply (Basics) 133 5.3 Vectorized Functions 138 5.4 Lambda Functions (Anonymous Functions) 141 Conclusion 142 Part II: Data Processing 143 Chapter 6. Data Assembly 145 Learning Objectives 145 6.1 Combine Data Sets 145 6.2 Concatenation 146 6.3 Observational Units Across Multiple Tables 154 6.4 Merge Multiple Data Sets 160 Conclusion 167 Chapter 7. Data Normalization 169 Learning Objectives 169 7.1 Multiple Observational Units in a Table (Normalization) 169 Conclusion 173 Chapter 8. Groupby Operations: Split-Apply-Combine 175 Learning Objectives 175 8.1 Aggregate 176 8.2 Transform 184 8.3 Filter 188 8.4 The pandas.core.groupby.DataFrameGroupBy object 190 8.5 Working with a MultiIndex 195 Conclusion 199 Part III: Data Types 203 Chapter 9. Missing Data 203 Learning Objectives 203 9.1 What Is a NaN Value? 203 9.2 Where Do Missing Values Come From? 205 9.3 Working with Missing Data 210 9.4 Pandas Built-In NA Missing 216 Conclusion 218 Chapter 10. Data Types 219 Learning Objectives 219 10.1 Data Types 219 10.2 Converting Types 220 10.3 Categorical Data 225 Conclusion 227 Chapter 11. Strings and Text Data 229 Introduction 229 Learning Objectives 229 11.1 Strings 229 11.2 String Methods 233 11.3 More String Methods 234 11.4 String Formatting (F-Strings) 236 11.5 Regular Expressions (RegEx) 239 11.6 The regex Library 247 Conclusion 247 Chapter 12. Dates and Times 249 Learning Objectives 249 12.1 Python's datetime Object 249 12.2 Converting to datetime 250 12.3 Loading Data That Include Dates 253 12.4 Extracting Date Components 254 12.5 Date Calculations and Timedeltas 257 12.6 Datetime Methods 259 12.7 Getting Stock Data 261 12.8 Subsetting Data Based on Dates 263 12.9 Date Ranges 266 12.10 Shifting Values 270 12.11 Resampling 276 12.12 Time Zones 278 12.13 Arrow for Better Dates and Times 280 Conclusion 280 Part IV: Data Modeling 281 Chapter 13. Linear Regression (Continuous Outcome Variable) 283 13.1 Simple Linear Regression 283 13.2 Multiple Regression 287 13.3 Models with Categorical Variables 289 13.4 One-Hot Encoding in scikit-learn with Transformer Pipelines 294 Conclusion 296 Chapter 14. Generalized Linear Models 297 About This Chapter 297 14.1 Logistic Regression (Binary Outcome Variable) 297 14.2 Poisson Regression (Count Outcome Variable) 304 14.3 More Generalized Linear Models 308 Conclusion 309 Chapter 15. Survival Analysis 311 15.1 Survival Data 311 15.2 Kaplan Meier Curves 312 15.3 Cox Proportional Hazard Model 314 Conclusion 317 Chapter 16. Model Diagnostics 319 16.1 Residuals 319 16.2 Comparing Multiple Models 324 16.3 k-Fold Cross-Validation 329 Conclusion 334 Chapter 17. Regularization 335 17.1 Why Regularize? 335 17.2 LASSO Regression 337 17.3 Ridge Regression 338 17.4 Elastic Net 340 17.5 Cross-Validation 341 Conclusion 343 Chapter 18. Clustering 345 18.1 k-Means 345 18.2 Hierarchical Clustering 351 Conclusion 356 Part V. Conclusion 357 Chapter 19. Life Outside of Pandas 359 19.1 The (Scientific) Computing Stack 359 19.2 Performance 360 19.3 Dask 360 19.4 Siuba 360 19.5 Ibis 361 19.6 Polars 361 19.7 PyJanitor 361 19.8 Pandera 361 19.9 Machine Learning 361 19.10 Publishing 362 19.11 Dashboards 362 Conclusion 362 Chapter 20. It's Dangerous To Go Alone! 363 20.1 Local Meetups 363 20.2 Conferences 363 20.3 The Carpentries 364 20.4 Podcasts 364 20.5 Other Resources 365 Conclusion 365 Appendices 367 A. Concept Maps 369B. Installation and Setup 373C. Command Line 377D. Project Templates 379E. Using Python 381F. Working Directories 383G. Environments 385H. Install Packages 389I. Importing Libraries 391J. Code Style 393K. Containers: Lists, Tuples, and Dictionaries 395L. Slice Values 399M. Loops 401N. Comprehensions 403O. Functions 405P. Ranges and Generators 409Q. Multiple Assignment 413R. Numpy ndarray 415S. Classes 417T. SettingWithCopyWarning 419U. Method Chaining 423V. Timing Code 427W. String Formatting 429X. Conditionals (if-elif-else) 433Y. New York ACS Logistic Regression Example 435Z. Replicating Results in R 443 Index 451
£34.19
O'Reilly Media Streaming Databases
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Nova Science Publishers Inc Data Storage: Systems, Management & Security
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Technics Publications LLC Data Resource Guide: Managing the Data Resource
Book SynopsisAre you struggling to find the data that you need to support your business activities? Are you concerned that people may be using the wrong data for their business activities? Are you having difficulty understanding the data that you do find in your data resource? Are you frustrated over documenting that understanding in a manner that is readily accessible to anyone in the organisation? If the answer to any of these questions is Yes, then you need to read "Data Resource Guide" to help identify, understand, access, and use the appropriate data. Most public and private sector organisations today have no formal, single location for the complete documentation of their data resource that is readily available to everyone in the organisation. Many organisations do not even have a concept of how to design, develop, or manage a single repository containing an understanding all the data available to the organisation. Yet they are staking their business on those data. "Data Resource Data" provided the complete data resource model for an organisation''s Data Resource Data. Data Resource Understanding provided a detailed description of how to thoroughly understand an organisation''s data resource through those Data Resource Data. Now, "Data Resource Guide" provides the detailed specifications for developing a simple, inexpensive, and effective way to document the data resource understanding and make that understanding readily available to anyone in the organisation. Michael Brackett draws on over half a century of data management experience to complete two trilogies for formally managing an organisation''s data as a critical resource. The Data Architecture Trilogy describes the development of a single organisation wide data architecture for an organization. The Data Understanding Trilogy describes the acquisition and documentation of understanding about all the data at an organisation''s disposal.
£36.89
Technics Publications LLC Data Lake Architecture: Designing the Data Lake
Book SynopsisOrganizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.
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Technics Publications LLC Growing Business Intelligence: An Agile Approach
Book SynopsisHow do we enable our organisations to enjoy the often significant benefits of BI and analytics, while at the same time minimising the cost and risk of failure? In this book, I am not going to try to be prescriptive; I wont tell you exactly how to build your BI environment. Instead, I am going to focus on a few core principles that will enable you to navigate the rocky shoals of BI architecture and arrive at a destination best suited for your particular organisation. Some of these core principles include: Have an overarching strategy, plan, and roadmap. Recognise and leverage your existing technology investments. Support both data discovery and data reuse. Keep data in motion, not at rest. Separate information delivery from data storage. Emphasise data transparency over data quality. Take an agile approach to BI development. This book will show you how to successfully navigate both the jungle of BI technology and the minefield of human nature. It will show you how to create a BI architecture and strategy that addresses the needs of all organisational stakeholders. It will show you how to maximise the value of your BI investments. It will show you how to manage the risk of disruptive technology. And it will show you how to use agile methodologies to deliver on the promise of BI and analytics quickly, succinctly, and iteratively. This book is about many things. But principally, its about success. The goal of any enterprise initiative is to succeed and to derive benefit -- benefit that all stakeholders can share in. I want you to be successful. I want your organisation to be successful. This book will show you how. This book is for anyone who is currently or will someday be working on a BI, analytics, or Big Data project, and for organisations that want to get the maximum amount of value from both their data and their BI technology investment. This includes all stakeholders in the BI effort -- not just the data people or the IT people, but also the business stakeholders who have the responsibility for the definition and use of data. There are six sections to this book: In Section I, What Kind of Garden Do You Want?, we will examine the benefits and risks of Business Intelligence, making the central point that BI is a business (not IT) process designed to manage data assets in pursuit of enterprise goals. We will show how data, when properly managed and used, can be a key enabler of several types of core business processes. The purpose of this section is to help you define the particular benefit(s) you want from BI. In Section II, Building the Bones, we will talk about how to design and build out the hardscape (infrastructure) of your BI environment. This stage of the process involves leveraging existing technology investments and iteratively moving toward your desired target state BI architecture. In Section III, From the Ground Up, we explore the more detailed aspects of implementing your BI operational environment. In Section IV, Weeds, Pests and Critters, we talk about the myriad of things that can go wrong on a BI project, and discuss ways of mitigating these risks. In Section V, The Sustainable Garden, we talk about how to create a BI infrastructure that is easy and inexpensive to maintain. Finally, Section VI presents a case study illustrating the principles of this book, as applied to a fictional manufacturing company (the Blue Moon Guitar Company).
£39.09
Technics Publications LLC Analytics: How to Win with Intelligence
Book SynopsisLearn how big data and other sources of information can be transformed into valuable knowledge -- knowledge that can create incredible competitive advantage to propel a business toward market leadership. Learn through examples and experience exactly how to pick projects and build analytics teams that deliver results. Know the ethical and privacy issues, and apply the three-part litmus test of context, permission, and accuracy. Without a doubt, data and analytics are the new source of competitive advantage, but how do executives go from hype to action? Thats the objective of this book -- to assist executives in making the right investments in the right place and at the right time in order to reap the full benefits of data analytics.
£27.89
Technics Publications LLC Data Resource Integration: Understanding & Resolving a Disparate Data Resource
Book SynopsisAre you struggling with a disparate data resource? Are there multiple existences of the same business fact scattered throughout the data resource? Are those multiple existences out of synch with each other? Do you have difficulty finding the data you need to support business activities? Do the data you find have poor quality? If the answer to any of these questions is Yes, then you need this book to guide you toward creating an integrated data resource. Most public and private sector organisations have a disparate data resource that was created over many years. That disparate data resource contains multiple existences of business facts that are out of synch with each other, are of poor quality, and are difficult to locate. The traditional approach to dealing with a disparate data resource is to perform periodic and temporary data integration to support a specific application or business activity. Those piecemeal data integration efforts may meet a current need, but seldom solve the underlying problems with a disparate data resource, and sometimes make the situation worse. This book explains how to go about understanding and resolving a disparate data resource and creating a comparate data resource that fully meets an organisation''s current and future business information demand. It builds on Data Resource Simplexity, which described how to stop the burgeoning data disparity. It explains the concepts, principles, and techniques for understanding a disparate data resource within the context of a common data architecture, and resolving that disparity with minimum impact on the business. Like Data Resource Simplexity, Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data resources in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, and biology, and applies them to the process of understanding and resolving a disparate data resource. He shows you how to approach and resolve a disparate data resource, and build a comparate data resource that fully supports the business.
£61.19
Technics Publications LLC Data Resource Design: Reality Beyond Illusion
Book SynopsisAre you struggling with the formal design of your organisation''s data resource? Do you find yourself forced into generic data architectures and universal data models? Do you find yourself warping the business to fit a purchased application? Do you find yourself pushed into developing physical databases without formal logical design? Do you find disparate data throughout the organisation? If the answer to any of these questions is Yes, then you need to read Data Resource Design to help guide you through a formal design process that produces a high quality data resource within a single common data architecture. Most public and private sector organisations do not consistently follow a formal data resource design process that begins with the organisation''s perception of the business world, proceeds through logical data design, through physical data design, and into implementation. Most organisations charge ahead with physical database implementation, physical package implementation, and other brute-force-physical approaches. The result is a data resource that becomes disparate and does not fully support the organisation in its business endeavours. This book describes how to formally design an organisation''s data resource to meet its current and future business information demand. It builds on Data Resource Simplexity, which described how to stop the burgeoning data disparity, and on Data Resource Integration, which described how to understand and resolve an organisation''s disparate data resource. It describes the concepts, principles, and techniques for building a high quality data resource based on an organisation''s perception of the business world in which they operate. Like Data Resource Simplexity and Data Resource Integration, Michael Brackett draws on five decades of data management experience building and managing data resources, and resolving disparate data in both public and private sector organisations. He leverages theories, concepts, principles, and techniques from a wide variety of disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to properly designing data as a critical resource of an organisation. He shows how to understand the business environment where an organisation operates and design a data resource that supports the organisation in that business environment.
£43.34
Technics Publications LLC Extreme Scoping: An Agile Approach to Enterprise Data Warehousing & Business Intelligence
Book SynopsisDo your business intelligence (BI) projects take too long to deliver? Is the value of the deliverables less than satisfactory? Do these projects propagate poor data management practices? If you screamed yes to any of these questions, read this book to master a proven approach to building your enterprise data warehouse and BI initiatives. Extreme Scoping, based on the Business Intelligence Roadmap, will show you how to build analytics applications rapidly yet not sacrifice data management and enterprise architecture. In addition, all of the roles required to deliver all seven steps of this agile methodology are explained along with many real-world examples. From Wayne Eckerson''s Foreword -- I''ve read many books about data warehousing and business intelligence (BI). This book by Larissa Moss is one of the best. I should not be surprised. Larissa has spent years refining the craft of designing, building, and delivering BI applications. Over the years, she has developed a keen insight about what works and doesn''t work in BI. This book brings to light the wealth of that development experience. Best of all, this is not some dry text that laboriously steps readers through a technical methodology. Larissa expresses her ideas in a clear, concise, and persuasive manner. I highlighted so many beautifully written and insightful paragraphs in her manuscript that it became comical. I desperately wanted the final, published book rather than the manuscript so I could dog-ear it to death and place it front-and-center in my office bookshelf! From David Well''s Foreword : Extreme Scoping is rich with advice and guidance for virtually every aspect of BI projects from planning and requirements to deployment and from back-end data management to front-end information and analytics services. Larissa is both a pragmatist and an independent thinker. Those qualities come through in the style of this book. This is a well-written book that is easy to absorb. It is not full of surprises. It is filled with a lot of common sense and lessons learned through experience.
£43.34
Springer High Performance Discovery in Time Series Techniques and Case Studies Monographs in Computer Science
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Springer Computer Network Security
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Springer-Verlag New York Inc. The Herschel Objects and How to Observe Them
Book SynopsisAmateur astronomers are always on the lookout for new observing challenges. This is a practical guide to locating and viewing the most impressive of Herschel’s star clusters, nebulae and galaxies, cataloging more than 600 of the brightest objects, and offering detailed descriptions and images of 150 to 200 of the best.Trade ReviewFrom the reviews: "Mullaney packs an incredible amount of information into this 166-page book. … All in all, The Herschel Objects, and how to observe them is engaging, challenging, well-written, and comprehensive. So, if you love deep-sky observing – and even if you’ve observed the Astronomical League’s Herschel 400 – Mullaney’s book offers a new list with several hundred additional objects you’ll enjoy." (Michael Bakich, Astronomy Magazine, October, 2007) "The Herschel Objects and How to Observe Them is a fine addition to the Springer series of observing guides. Mullaney has been observing the Herschel objects for many years and his passion for them clearly comes across. … Overall though, this is a book that will be a useful addition to any deep-sky observer’s library." (Paul Money, BBC Sky at Night, February, 2008) "Mullaney begins with a well-written biographical sketch of Herschel and his family, and explains the significance of the work of this great observational astronomer. … the objects are illustrated with excellent images obtained using a modern charge-coupled device (CCD) system. The book concludes with a list of 618 targets that would provide for a lifetime of study. The book will be of greatest interest to experienced observers who wish to push on to the most challenging deep sky objects. … Summing Up: Recommended. General readers." (D. E. Hogg, CHOICE, Vol. 45 (6), February, 2008) "The book opens with a few short chapters on Herschel himself together with a brief introduction to observing techniques … . rounded out with some objects that the author regards as showpieces that were not discovered by Herschel. Any collection of these will of course be very subjective. … I found the book’s reproductions to be a cut above the usual Springer ones and the book does offers something sufficiently different … and the Astronomical League guides to make it worth adding to your collection." (Owen Brazell, The Observatory, Vol. 128 (1203), 2008)Table of ContentsWilliam Herschel's Life, Telescopes and Catalogs.- Herschel's Telescopes.- Herschel's Catalogs and Classes.- Observing Techniques.- Exploring The Herschel Showpieces.- Showpieces of Class I.- Showpieces of Class IV.- Showpieces of Class V.- Showpieces of Class VI.- Showpieces of Class VII.- Showpieces of Class VIII.- Samples of Classes II & III.- Showpieces Missed by Herschel.- The “Missing” Herschel Objects.- Conclusion.
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Springer Data Mining for Association Rules and Sequential Patterns Sequential and Parallel Algorithms
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Springer Survey of Text Mining Clustering Classification and Retrieval No 1
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Springer Adaptive Hypertext and Hypermedia
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Springer Syntactic Wordclass Tagging 9 Text Speech and Language Technology
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Springer Advances in Intelligent Systems
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Springer Unconventional Optical Elements for Information Storage Processing and Communications Proceedings of the NATO Advanced Research Workshop Tel Aviv Tel Aviv Israel October 1921 1999 75
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Springer Unconventional Optical Elements for Information Storage Processing and Communications Proceedings of the NATO Advanced Research Workshop Tel Aviv 75 Nato Science Partnership Subseries 3
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Springer Mathematical Foundations of Information Retrieval
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