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Data stream management = processing ...

Garofalakis, Minos.

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Data stream management = processing high-speed data streams /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data stream management/ edited by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi.
Reminder of title:	processing high-speed data streams /
other author:	Garofalakis, Minos.
Published:	Berlin, Heidelberg :Springer Berlin Heidelberg : : 2016.,
Description:	vii, 537 p. :ill., digital ;24 cm.
[NT 15003449]:	Part I: Introduction -- Part II: Computation of Basic Stream Synopses -- Part III: Mining Data Streams -- Part IV: Advanced Topics -- Part V: Systems and Architectures -- Part VI: Applications.
Contained By:	Springer eBooks
Subject:	Streaming technology (Telecommunications) -
Online resource:	http://dx.doi.org/10.1007/978-3-540-28608-0
ISBN:	9783540286080

Data stream management = processing high-speed data streams /
Data stream managementprocessing high-speed data streams /[electronic resource] :edited by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi. - Berlin, Heidelberg :Springer Berlin Heidelberg :2016. - vii, 537 p. :ill., digital ;24 cm. - Data-centric systems and applications,2197-9723. - Data-centric systems and applications..

Part I: Introduction -- Part II: Computation of Basic Stream Synopses -- Part III: Mining Data Streams -- Part IV: Advanced Topics -- Part V: Systems and Architectures -- Part VI: Applications.

We live in the era of "Big Data": Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets) Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics) Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.

ISBN: 9783540286080

Standard No.: 10.1007/978-3-540-28608-0doiSubjects--Topical Terms:

603808
Streaming technology (Telecommunications)

LC Class. No.: TK5105.386

Dewey Class. No.: 006.7876

Data stream management = processing high-speed data streams /
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245 0 0 $a Data stream management $h [electronic resource] : $b processing high-speed data streams / $c edited by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi.
260 $a Berlin, Heidelberg : $b Springer Berlin Heidelberg : $b Imprint: Springer, $c 2016.
300 $a vii, 537 p. : $b ill., digital ; $c 24 cm.
490 1 $a Data-centric systems and applications, $x 2197-9723
505 0 $a Part I: Introduction -- Part II: Computation of Basic Stream Synopses -- Part III: Mining Data Streams -- Part IV: Advanced Topics -- Part V: Systems and Architectures -- Part VI: Applications.
520 $a We live in the era of "Big Data": Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets) Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics) Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud computing, big data analytics, and complex event processing), and a discussion of future directions in this exciting field. The book provides a comprehensive overview of core concepts and technological foundations, as well as various systems and applications, and is of particular interest to students, lecturers and researchers in the area of data stream management.
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650 2 4 $a Database Management. $3 891010
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Big Data/Analytics. $3 2186785
650 2 4 $a Data Structures. $3 891009
650 2 4 $a Information Storage and Retrieval. $3 761906
700 1 $a Garofalakis, Minos. $3 2203181
700 1 $a Gehrke, Johannes. $3 2203182
700 1 $a Rastogi, Rajeev. $3 2203183
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