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Le Big Data est omniprésent dans les médias. Qualifié de source d?innovation, de richesses, de création d?emplois, d?enjeu démocratique quand il est ± open , le Big Data fascine et effraye à la fois. Mais de quoi parle-t-on exactement ? Ces données massives sont-elles du seul domaine des informaticiens, des statisticiens, des politiques et des créateurs d?entreprises ? Les professionnels de l?information-documentation n?ont-ils pas un rôle à jouer dans ce nouveau paysage : identification, qualification, archivage, classification ? Cet ouvrage rassemble les contributions de spécialistes issus de diverses disciplines et réunis au colloque Inria en octobre 2014. Dans le flou lié à la mutation profonde que connaît actuellement le paysage informationnel, ils donnent les clés pour appréhender ce nouveau domaine et pour percevoir la place réservée aux compétences métier de l?information-documentation.
Information science --- Natural language processing (Computer science) --- Sciences de l'information --- Traitement automatique des langues naturelles --- Technological innovations --- Innovations --- Big data --- Data mining --- Machine theory --- Communication --- Online social networks --- Information technology --- Information society --- Social aspects --- Données massives --- Traitement automatique du langage naturel --- Congresses --- Distributed data bases --- Données massives. --- Traitement automatique du langage naturel. --- Communication - Social aspects --- Information technology - Social aspects --- Données massives.
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Distributed databases --- Bases de données réparties --- Avalon --- Camelot (Computer file) --- 681.3*C24 --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Distributed systems: distributed databases; distributed applications; networkoperating systems --- 681.3*C24 Distributed systems: distributed databases; distributed applications; networkoperating systems --- Bases de données réparties --- Avalon. --- Carnegie Mellon low overhead transaction facility
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Distributed databases --- Parallel processing (Electronic computers) --- Bases de données réparties --- Parallélisme (Informatique) --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Information Technology --- Mathematical Sciences --- Computer Architecture, Languages and Compilers --- Data Storage and Data Mining --- Database Design & Management --- Applied Mathematics
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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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Distributed databases. --- Transborder data flow. --- Communication, International. --- Bases de données réparties --- Flux transfrontières de données --- Communication internationale --- Communication, International --- Distributed databases --- Transborder data flow --- #TELE:MI2 --- Data flow, Transborder --- Data flow, Transboundary --- Data flow, Transnational --- Data flow across national borders --- Transboundary data flow --- Transnational data flow --- Electronic data processing --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- International communication --- World communication --- Communication --- International cooperation
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Expert Oracle RAC Performance Diagnostics and Tuning provides comprehensive coverage of the features, technology and principles for testing and tuning RAC databases. The book takes a deep look at optimizing RAC databases by following a methodical approach based on scientific analysis rather than using a speculative approach, twisting and turning knobs and gambling on the system. The book starts with the basic concepts of tuning methodology, capacity planning, and architecture. Author Murali Vallath then dissects the various tiers of the testing implementation, including the operating system, the network, the application, the storage, the instance, the database, and the grid infrastructure. He also introduces tools for performance optimization and thoroughly covers each aspect of the tuning process, using many real-world examples, analyses, and solutions from the field that provide you with a solid, practical, and replicable approach to tuning a RAC environment. The book concludes with troubleshooting guidance and quick reference of all the scripts used in the book. Expert Oracle RAC Performance Diagnostics and Tuning covers scenarios and details never discussed before in any other performance tuning books. If you have a RAC database, this book is a requirement. Get your copy today. Takes you through optimizing the various tiers of the RAC environment. Provides real life case studies, analysis and solutions from the field. Maps a methodical approach to testing, tuning and diagnosing the cluster.
Oracle (Computer file) --- Oracle7.3 --- Oracle 7.3 --- Personal Oracle7 --- Personal Oracle 7 --- Database management. --- Distributed databases. --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- 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 --- Electronic data processing --- Data structures (Computer scienc. --- Database Management. --- Data Structures and Information Theory. --- Data structures (Computer science). --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science)
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Beginning Apache Cassandra Development introduces you to one of the most robust and best-performing NoSQL database platforms on the planet. Apache Cassandra is a document database following the JSON document model. It is specifically designed to manage large amounts of data across many commodity servers without there being any single point of failure. This design approach makes Apache Cassandra a robust and easy-to-implement platform when high availability is needed. Apache Cassandra can be used by developers in Java, PHP, Python, and JavaScript—the primary and most commonly used languages. In Beginning Apache Cassandra Development, author and Cassandra expert Vivek Mishra takes you through using Apache Cassandra from each of these primary languages. Mishra also covers the Cassandra Query Language (CQL), the Apache Cassandra analog to SQL. You'll learn to develop applications sourcing data from Cassandra, query that data, and deliver it at speed to your application's users. Cassandra is one of the leading NoSQL databases, meaning you get unparalleled throughput and performance without the sort of processing overhead that comes with traditional proprietary databases. Beginning Apache Cassandra Development will therefore help you create applications that generate search results quickly, stand up to high levels of demand, scale as your user base grows, ensure operational simplicity, and—not least—provide delightful user experiences.
Distributed databases. --- Apache Cassandra. --- Cassandra (Electronic resource) --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- Database management. --- Information systems. --- Data mining. --- Database Management. --- Computer Appl. in Administrative Data Processing. --- Data Mining and Knowledge Discovery. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 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 --- Electronic data processing --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software
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Bases de donnees reparties --- Bases de données à objets --- Distributed data bases --- Gespreide data bases --- Object-georiënteerde data bases --- Object-oriented data bases --- Object-oriented databases --- Systeemontwerp --- System design --- Systemes [Conception de] --- Distributed databases --- Bases de données réparties --- Systèmes, Conception de --- Basic Sciences. Biology --- Evolution and Phylogeny --- Distributed databases. --- Object-oriented databases. --- System design. --- Evolution and Phylogeny. --- Conception de systèmes --- Conception de systèmes informatiques --- Design [System ] --- Systems design --- Systèmes [Conception de] --- Systèmes informatiques -- Conception et construction --- Bases de données réparties --- Bases de données à objets --- Systèmes, Conception de --- Crops --- Evolution --- Origin --- Plants --- Crops - Evolution. --- Crops - Origin. --- Plants - Evolution.
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Database management --- Distributed databases --- Bases de données --- Bases de données réparties --- Gestion --- 681.3*H2 --- Database management. --- Distributed data bases --- Distributed database systems --- Databases --- Cyberinfrastructure --- 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 --- Electronic data processing --- Database management: security; integrity; protection--See also {?681.5*E5} --- 681.3*H2 Database management: security; integrity; protection--See also {?681.5*E5} --- Bases de données --- Bases de données réparties
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