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Titlebook: Data Stream Management; Processing High-Spee Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi Textbook 2016 Springer-Verlag Berlin Heidelbe

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發(fā)表于 2025-3-21 17:51:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Stream Management
副標(biāo)題Processing High-Spee
編輯Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi
視頻videohttp://file.papertrans.cn/264/263155/263155.mp4
概述Comprehensive introduction to the algorithmic and theoretical foundations of data stream processing – from basic mathematical models, algorithms, and analytics, and progressing to more advanced stream
叢書名稱Data-Centric Systems and Applications
圖書封面Titlebook: Data Stream Management; Processing High-Spee Minos Garofalakis,Johannes Gehrke,Rajeev Rastogi Textbook 2016 Springer-Verlag Berlin Heidelbe
描述.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 processingalgorithms, 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
出版日期Textbook 2016
關(guān)鍵詞Data streams; Data model extensions; Database management system engines; Data mining; Sensor networks; XM
版次1
doihttps://doi.org/10.1007/978-3-540-28608-0
isbn_softcover978-3-662-56837-8
isbn_ebook978-3-540-28608-0Series ISSN 2197-9723 Series E-ISSN 2197-974X
issn_series 2197-9723
copyrightSpringer-Verlag Berlin Heidelberg 2016
The information of publication is updating

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Quantiles and Equi-depth Histograms over Streams the 99-percentile, or the quartiles of a set are examples of quantile queries. Many database optimization problems involve approximate quantile computations over large data sets. Query optimizers use quantile estimates to estimate the size of intermediate results and choose an efficient plan among
地板
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Join Sizes, Frequency Moments, and Applicationslem is at the heart of a wide variety of other problems, both in databases/data streams and beyond, including approximating range-query aggregates, quantiles, and heavy-hitter elements, and building approximate histograms and wavelet representations. Our discussion focuses on efficient, sketch-based
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Top-, Frequent Item Maintenance over Streamsthat occur most frequently in one pass over the data stream using a small amount of storage space. Such problems arise in a variety of settings. For example, a search engine might be interested in gathering statistics about its query stream and in particular, identifying the most popular queries. An
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Clustering Data Streamsch that, under some definition of “similarity,” similar items are in the same group and dissimilar items are in different groups. In this chapter we focus on clustering in a streaming scenario where a small number of data items are presented at a time and we cannot store all the data points. Thus, o
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發(fā)表于 2025-3-23 01:54:38 | 只看該作者
Mining Decision Trees from Streams. Mining these continuous data streams brings unique opportunities, but also new challenges. We present a method that can semi-automatically enhance a wide class of existing learning algorithms so they can learn from such high-speed data streams in real time. The method works by sampling just enough
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