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Some researcher has created the vision of the 'data utility' as a key enabler towards ubiquitous and pervasive computing. Decentralization and replication would be the approach to make it resistant against security attacks. This book presents an organic view on the research and technologies, which bring us towards the realization of the vision.

Database management. --- Database management --- Distributed databases --- Engineering & Applied Sciences --- Computer Science --- Distributed databases. --- Distributed data bases --- Distributed database systems --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Databases --- Cyberinfrastructure --- Electronic data processing

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Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings.

Data mining. --- Distributed databases. --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Data mining --- Exploration de données (Informatique) --- Bases de données réparties

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Take a journey toward discovering, learning, and using Apache Spark 3.0. In this book, you will gain expertise on the powerful and efficient distributed data processing engine inside of Apache Spark; its user-friendly, comprehensive, and flexible programming model for processing data in batch and streaming; and the scalable machine learning algorithms and practical utilities to build machine learning applications. Beginning Apache Spark 3 begins by explaining different ways of interacting with Apache Spark, such as Spark Concepts and Architecture, and Spark Unified Stack. Next, it offers an overview of Spark SQL before moving on to its advanced features. It covers tips and techniques for dealing with performance issues, followed by an overview of the structured streaming processing engine. It concludes with a demonstration of how to develop machine learning applications using Spark MLlib and how to manage the machine learning development lifecycle. This book is packed with practical examples and code snippets to help you master concepts and features immediately after they are covered in each section. After reading this book, you will have the knowledge required to build your own big data pipelines, applications, and machine learning applications. What You Will Learn Master the Spark unified data analytics engine and its various components Work in tandem to provide a scalable, fault tolerant and performant data processing engine Leverage the user-friendly and flexible programming model to perform simple to complex data analytics using dataframe and Spark SQL Develop machine learning applications using Spark MLlib Manage the machine learning development lifecycle using MLflow Who This Book Is For Data scientists, data engineers and software developers.

Spark (Electronic resource : Apache Software Foundation) --- Big data. --- Distributed databases. --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Data sets, Large --- Large data sets --- Data sets --- Open source software. --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Free software (Open source software) --- Open code software --- Opensource software --- Computer software --- Apache Spark (Electronic resource : Apache Software Foundation)

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This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available.

Distributed databases. --- Databases. --- Data banks --- Data bases --- Databanks --- Database systems --- Computer files --- Electronic information resources --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Computer network architectures. --- Information Systems Applications (incl. Internet). --- Computer Systems Organization and Communication Networks. --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Application software. --- Computer organization. --- Organization, Computer --- Electronic digital computers --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Computer science.

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Advanced data management has always been at the core of efficient database and information systems. Recent trends like big data and cloud computing have aggravated the need for sophisticated and flexible data storage and processing solutions.This book provides a comprehensive coverage of the principles of data management developed in the last decades with a focus on data structures and query languages. It treats a wealth of different data models and surveys the foundations of structuring, processing, storing and querying data according these models. Starting off with the topic of database design, it further discusses weaknesses of the relational data model, and then proceeds to convey the basics of graph data, tree-structured XML data, key-value pairs and nested, semi-structured JSON data, columnar and record-oriented data as well as object-oriented data. The final chapters round the book off with an analysis of fragmentation, replication and consistency strategies for data management in distributed databases as well as recommendations for handling polyglot persistence in multi-model databases and multi-database architectures. While primarily geared towards students of Master-level courses in Computer Science and related areas, this book may also be of benefit to practitioners looking for a reference book on data modeling and query processing. It provides both theoretical depth and a concise treatment of open source technologies currently on the market.

Distributed databases. --- Relational databases. --- Non-relational databases. --- Cloud computing. --- SQL (Computer program language) --- Structured Query Language (Computer program language) --- Declarative programming languages --- Query languages (Computer science) --- Electronic data processing --- Web services --- Nonrelational databases --- NoSQL databases --- Databases --- Relational data bases --- Distributed data bases --- Distributed database systems --- Cyberinfrastructure --- Distributed processing --- Distributed databases --- Relational databases --- Non-relational databases --- Cloud computing --- E-books --- Data models. --- Data storage. --- NoSQL databases. --- SQL databases.