Database programming Books
Mike Murach & Associates Inc. Murachs Oracle SQL & Pl / SQL for Developers
Book SynopsisIf you are developing applications that access Oracle databases, you can save time and trouble by having the database do more work for you. That is where this book comes in. It teaches you how to create effective SQL queries to retrieve and update the data in an Oracle database. It teaches you how to design and implement a database, giving you insight into performance and security issues. It teaches you how to use Oracle''s procedural language, PL/SQL, to take advantage of powerful features like stored procedures, functions, and triggers. In short, it teaches you to write the kind of SQL and PL/SQL that makes you a more effective and valuable developer.
£44.79
Mike Murach & Associates Inc. Murachs Visual Basic 2015
Book Synopsis
£44.24
Simple Talk Publishing Dissecting SQL Server Execution Plans
£17.99
Alakai Publishing LLC Scrum Mastery
Book Synopsis
£999.99
Springer Nature Switzerland AG Advances in Intelligent Data Analysis XVIII: 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings
Book SynopsisThis open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions. Advancing Intelligent Data Analysis requires novel, potentially game-changing ideas. IDA’s mission is to promote ideas over performance: a solid motivation can be as convincing as exhaustive empirical evaluation.Table of ContentsMultivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder.- Dual Sequential Variational Autoencoders for Fraud Detection.- A Principled Approach to Analyze Expressiveness and Accuracy of Graph Neural Networks.- Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams.- GraphMDL: Graph Pattern Selection Based on Minimum Description Length.- Towards Content Sensitivity Analysis.- Gibbs Sampling Subjectively Interesting Tiles.- Even Faster Exact k-Means Clustering.- Ising-Based Consensus Clustering on Special Purpose Hardware.- Transfer Learning by Learning Projections from Target to Source.- Computing Vertex-Vertex Dissimilarities Using Random Trees: Application to Clustering in Graphs.- Towards Evaluation of CNN Performance in Semantically Meaningful Latent Spaces.- Vouw: Geometric Pattern Mining Using the MDL Principle.- A Consensus Approach to Improve NMF Document Clustering.- Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams.- Widening for MDL-Based Retail Signature Discovery.- Addressing the Resolution Limit and the Field of View Limit in Community Mining.- Estimating Uncertainty in Deep Learning for Reporting Confidence: An Application on Cell Type Prediction in Testes Based on Proteomics.- Adversarial Attacks Hidden in Plain Sight.- Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code.- Overlapping Hierarchical Clustering (OHC).- Digital Footprints of International Migration on Twitter.- Percolation-Based Detection of Anomalous Subgraphs in Complex Networks.- A Late-Fusion Approach to Community Detection in Attributed Networks.- Reconciling Predictions in the Regression Setting: an Application to Bus Travel Time Prediction.- A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization.- Actionable Subgroup Discovery and Urban Farm Optimization.- AVATAR - Machine Learning Pipeline Evaluation Using Surrogate Model.- Detection of Derivative Discontinuities in Observational Data.- Improving Prediction with Causal Probabilistic Variables.- DO-U-Net for Segmentation and Counting.- Enhanced Word Embeddings for Anorexia Nervosa Detection on Social Media.- Event Recognition Based on Classification of Generated Image Captions.- Human-to-AI Coach: Improving Human Inputs to AI Systems.- Aleatoric and Epistemic Uncertainty with Random Forests.- Master your Metrics with Calibration.- Supervised Phrase-Boundary Embeddings.- Predicting Remaining Useful Life with Similarity-Based Priors.- Orometric Methods in Bounded Metric Data.- Interpretable Neuron Structuring with Graph Spectral Regularization.- Comparing the Preservation of Network Properties by Graph Embeddings.- Making Learners (More) Monotone.- Combining Machine Learning and Simulation to a Hybrid Modelling Approach.- LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.- Angle-Based Crowding Degree Estimation for Many-Objective Optimization.
£34.99
Springer Nature Switzerland AG Data Warehousing and Analytics: Fueling the Data Engine
Book SynopsisThis textbook covers all central activities of data warehousing and analytics, including transformation, preparation, aggregation, integration, and analysis. It discusses the full spectrum of the journey of data from operational/transactional databases, to data warehouses and data analytics; as well as the role that data warehousing plays in the data processing lifecycle. It also explains in detail how data warehouses may be used by data engines, such as BI tools and analytics algorithms to produce reports, dashboards, patterns, and other useful information and knowledge.The book is divided into six parts, ranging from the basics of data warehouse design (Part I - Star Schema, Part II - Snowflake and Bridge Tables, Part III - Advanced Dimensions, and Part IV - Multi-Fact and Multi-Input), to more advanced data warehousing concepts (Part V - Data Warehousing and Evolution) and data analytics (Part VI - OLAP, BI, and Analytics).This textbook approaches data warehousing from the case study angle. Each chapter presents one or more case studies to thoroughly explain the concepts and has different levels of difficulty, hence learning is incremental. In addition, every chapter has also a section on further readings which give pointers and references to research papers related to the chapter. All these features make the book ideally suited for either introductory courses on data warehousing and data analytics, or even for self-studies by professionals. The book is accompanied by a web page that includes all the used datasets and codes as well as slides and solutions to exercises.Table of Contents1. Introduction.- Part I: Star Schema.- 2. Simple Star Schemas.- 3. Creating Facts and Dimensions: More Complex Processes.- Part II: Snowflake and Bridge Tables.- 4. Hierarchies.- 5. Bridge Tables.- 6. Temporal Data Warehousing.- Part III: Advanced Dimension.- 7. Determinant Dimensions.- 8. Junk Dimensions.- 9. Dimension Keys.- 10. One-Attribute Dimensions.- Part IV: Multi-Fact and Multi-Input.- 11. Multi-Fact Star Schemas.- 12. Slicing a Fact.- 13. Multi-Input Operational Databases.- Part V: Data Warehousing Granularity and Evolution.- 14. Data Warehousing Granularity and Levels of Aggregation.- 15. Designing Lowest-Level Star Schemas.- 16. Levels of Aggregation: Adding and Removing Dimensions.- 17. Levels of Aggregation and Bridge Tables.- 18. Active Data Warehousing.- Part VI: OLAP, Business Intelligence, and Data Analytics.- 19. Online Analytical Processing (OLAP).- 20. Pre- and Post-Data Warehousing.- 21. Data Analytics for Data Warehousing.
£58.49
Springer Fachmedien Wiesbaden Datenmanagement: Daten – Datenbanken –
Book SynopsisIm einführenden Kapitel dieses Datenmanagementbuchs geht es zunächst um die Frage, wie im Zuge der Digitalisierung die Umwandlung von analogen Daten in binäre Zahlen abläuft und welche Auswirkungen dies auf die computergesteuerte Verarbeitung von Texten, Bildern, Farben sowie Musik hat. Des Weiteren betrachten die Verfasser folgende Gesichtspunkte: Grundlagen von DatenbankenDatenbankentwürfeMalwareMögliche DatenschutzmaßnahmenTable of ContentsDigitale Daten - Dateien - Datenbanken - Datenschutz – Datensicherheit
£11.02
BPB Publications SQL Server Interview Questions
Book Synopsis
£14.99
BPB Publications Blockchain and DLT
Book SynopsisBlockchain secures individual transactions with cryptography, creating a tamper-proof chain. In contrast, distributed ledger technologies cover various technologies that may not adhere strictly to blockchain''s specific structure. We will start the book by defining blockchain elements, comparing it to DLT, exploring security mechanisms, and looking at Bitcoin''s history, transactions, wallets, keys, mining, and forensics. Further in the book, we will explore Ethereum''s evolution, Ethereum virtual machine, and smart contracts. We will also discover permissioned blockchains with Hyperledger Fabric and understand crypto assets, ERC20, ERC721 tokens, NFTs, and cryptocurrencies. With real-world examples and practical insights, you will gain a solid grasp of the fundamentals and practical applications that make blockchain a game-changer in various industries.
£29.92
Springer Verlag, Singapore Knowledge Technology: Converting Data and Information into Valuable Ideas
Book SynopsisThis book reviews creative technologies that underpin the human activities of converting data and information into knowledge, creating new ideas based on that knowledge, and validating those ideas. The book calls such creative technology “knowledge technology” and explores its challenges in the age of big data. Today, artificial intelligence and big data are successfully performing pattern recognition and decision making on behalf of humans, who are incapable of processing large amounts of information instantly and accurately. In using only past data, however, there are limits to future predictions with artificial intelligence and big data. Humans need to take the lead in predicting or creating the future by trying to understand why data emerged. Knowledge technology contributes to that understanding, regardless of the field of origin. As a matter of fact, knowledge technology includes many technologies developed in informatics, management study, and systems science. The desirable features of knowledge technology are the complementary use of rational and intuitive approaches, the integration of results from quantitative and qualitative analyses, and the cooperation between artificial intelligence and humans.Table of Contents-Preface.- 1. Introduction.- 2. Defining Knowledge Technology.- 3. Integrative Knowledge Technologies.- 4. Converting Data/Knowledge into Information.- 5. Converting Information into Knowledge.- 6. Creating New Ideas from the Knowledge.- 7. Validating the Value of the New Ideas.- 8. Summary and Expectations.
£49.49
