Data Stream Management (Record no. 84922)
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fixed length control field | 03438nam a22005055i 4500 |
001 - CONTROL NUMBER | |
control field | 978-3-031-01837-4 |
005 - DATE AND TIME OF LATEST TRANSACTION | |
control field | 20240730163728.0 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 220601s2010 sz | s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
ISBN | 9783031018374 |
-- | 978-3-031-01837-4 |
082 04 - CLASSIFICATION NUMBER | |
Call Number | 004.6 |
100 1# - AUTHOR NAME | |
Author | Golab, Lukasz. |
245 10 - TITLE STATEMENT | |
Title | Data Stream Management |
250 ## - EDITION STATEMENT | |
Edition statement | 1st ed. 2010. |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | VIII, 65 p. |
490 1# - SERIES STATEMENT | |
Series statement | Synthesis Lectures on Data Management, |
505 0# - FORMATTED CONTENTS NOTE | |
Remark 2 | Introduction -- Data Stream Management Systems -- Streaming Data Warehouses -- Conclusions. |
520 ## - SUMMARY, ETC. | |
Summary, etc | Many applications process high volumes of streaming data, among them Internet traffic analysis, financial tickers, and transaction log mining. In general, a data stream is an unbounded data set that is produced incrementally over time, rather than being available in full before its processing begins. In this lecture, we give an overview of recent research in stream processing, ranging from answering simple queries on high-speed streams to loading real-time data feeds into a streaming warehouse for off-line analysis. We will discuss two types of systems for end-to-end stream processing: Data Stream Management Systems (DSMSs) and Streaming Data Warehouses (SDWs). A traditional database management system typically processes a stream of ad-hoc queries over relatively static data. In contrast, a DSMS evaluates static (long-running) queries on streaming data, making a single pass over the data and using limited working memory. In the first part of this lecture, we will discuss research problems in DSMSs, such as continuous query languages, non-blocking query operators that continually react to new data, and continuous query optimization. The second part covers SDWs, which combine the real-time response of a DSMS by loading new data as soon as they arrive with a data warehouse's ability to manage Terabytes of historical data on secondary storage. Table of Contents: Introduction / Data Stream Management Systems / Streaming Data Warehouses / Conclusions. |
700 1# - AUTHOR 2 | |
Author 2 | Ozsu, M. Tamer. |
856 40 - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-01837-4 |
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Koha item type | eBooks |
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-- | Springer International Publishing : |
-- | Imprint: Springer, |
-- | 2010. |
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-- | computer |
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-- | online resource |
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-- | text file |
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer networks . |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data structures (Computer science). |
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Information theory. |
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Computer Communication Networks. |
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
-- | Data Structures and Information Theory. |
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
-- | 2153-5426 |
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-- | ZDB-2-SXSC |
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