Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi's Data Stream Management: Processing High-Speed Data Streams PDF

By Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi

ISBN-10: 3540286071

ISBN-13: 9783540286073

ISBN-10: 354028608X

ISBN-13: 9783540286080

This quantity makes a speciality of the speculation and perform of data flow management, and the radical demanding situations this rising area poses for data-management algorithms, platforms, and purposes. the gathering of chapters, contributed through experts within the box, bargains a complete advent to either the algorithmic/theoretical foundations of knowledge streams, in addition to the streaming structures and purposes in-built various domains.

A brief introductory bankruptcy presents a quick precis of a few uncomplicated info streaming suggestions and versions, and discusses the main parts of a customary flow question processing structure. hence, half I makes a speciality of easy streaming algorithms for a few key analytics services (e.g., quantiles, norms, subscribe to aggregates, heavy hitters) over streaming information. half II then examines very important thoughts for simple move mining initiatives (e.g., clustering, category, widespread itemsets). half III discusses a couple of complicated themes on move processing algorithms, and half IV makes a speciality of process and language facets of information circulate processing with surveys of influential process prototypes and language designs. half V then offers a few consultant purposes of streaming thoughts in numerous domain names (e.g., community administration, monetary analytics). eventually, the amount concludes with an summary of present facts streaming items and new program domain names (e.g. cloud computing, significant information analytics, and complicated occasion processing), and a dialogue of destiny instructions during this intriguing field.

The ebook presents a finished assessment of middle suggestions and technological foundations, in addition to a variety of platforms and functions, and is of specific curiosity to scholars, teachers and researchers within the quarter of knowledge move administration.

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Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi's Data Stream Management: Processing High-Speed Data Streams PDF

This quantity specializes in the idea and perform of information circulate administration, and the unconventional demanding situations this rising area poses for data-management algorithms, structures, and functions. the gathering of chapters, contributed by means of professionals within the box, deals a complete advent to either the algorithmic/theoretical foundations of knowledge streams, in addition to the streaming structures and purposes inbuilt various domain names.

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In Sect. 5, we will consider algorithms for this more general setting as well. We note here that a simple modification of the model above can be used to capture the turnstile case: the ith cell on the read-only tape contains both the ith element in the data sequence and an additional bit that indicates whether the element is being inserted or deleted. |S|] and outputs an element of rank r in S. We say that the order-statistic query is answered with -accuracy if the output element is guaranteed to have rank within r ± n.

15th VLDB (1989), pp. 269– 277 27. F. Olken, D. Rotem, Maintenance of materialized views of sampling queries, in Proc. Eighth ICDE (1992), pp. 632–641 28. F. Olken, D. Rotem, Sampling from spatial databases, in Proc. Ninth ICDE (1993), pp. 199– 208 29. F. Olken, D. Rotem, P. Xu, Random sampling from hash files, in Proc. ACM SIGMOD (1990), pp. 375–386 30. J. F. Naughton, S. N. Swami, Selectivity and cost estimation for joins based on random sampling. J. Comput. Syst. Sci. 52, 550–569 (1996) 31. J.

Data-Stream Sampling: Basic Techniques and Results 27 Thus the probability that element ei is in the final sample decreases geometrically as i decreases; the larger the value of p, the faster the rate of decrease. Chao [56] has extended the basic reservoir sampling algorithm to handle arbitrary sampling probabilities. Specifically, just after the processing of element ei , Chao’s scheme ensures that the inclusion probabilities satisfy Pr{ej ∈ S} ∝ rj for 1 ≤ j ≤ i, where {rj : j ≥ 1} is a prespecified sequence of positive numbers.

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Data Stream Management: Processing High-Speed Data Streams by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi


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