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

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Textbook 2016frequent 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

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Attilio Nebuloni,Giorgio Vignatin time instant and window size refers to N. This chapter presents a general technique, called the Exponential Histogram (EH) technique, that can be used to solve a wide variety of problems in the sliding-window model; typically problems that require us to maintain statistics. We will showcase this t

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Masatoshi Hamanaka,Keiji Hirata,Satoshi Tojothis chapter, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as

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Data Stream Management: A Brave New World,ry chapter, we provide a brief summary of some basic data streaming concepts and models, and discuss the key elements of a generic stream query processing architecture. We then give a short overview of the contents of this volume.

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The Sliding-Window Computation Model and Resultsn time instant and window size refers to N. This chapter presents a general technique, called the Exponential Histogram (EH) technique, that can be used to solve a wide variety of problems in the sliding-window model; typically problems that require us to maintain statistics. We will showcase this t

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Tracking Queries over Distributed Streamsthis chapter, we provide a brief introduction to the distributed data streaming model and the Geometric Method (GM), a generic technique for effectively tracking complex queries over massive distributed streams. We also discuss several recently-proposed extensions to the basic GM framework, such as

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Data Stream Management978-3-540-28608-0Series ISSN 2197-9723 Series E-ISSN 2197-974X

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https://doi.org/10.1007/978-3-319-60919-5am problems studied: In the mid-1980’s, Flajolet and Martin gave an effective algorithm that uses only logarithmic space. Recent work has built upon their technique, improving the accuracy guarantees on the estimation, proving lower bounds, and considering other settings such as sliding windows, distributed streams, and sensor networks.

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