By Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi
This quantity makes a speciality of the idea and perform of data movement management, and the unconventional demanding situations this rising area poses for data-management algorithms, platforms, and functions. the gathering of chapters, contributed by means of specialists within the box, deals a accomplished advent to either the algorithmic/theoretical foundations of information streams, in addition to the streaming platforms and purposes inbuilt diverse domains.
A brief introductory bankruptcy presents a short precis of a few simple facts streaming innovations and versions, and discusses the main parts of a well-known move question processing structure. to that end, 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 info. half II then examines vital recommendations for simple circulate mining initiatives (e.g., clustering, category, widespread itemsets). half III discusses a few complicated themes on circulate processing algorithms, and half IV specializes in procedure and language facets of knowledge move processing with surveys of influential process prototypes and language designs. half V then offers a few consultant functions of streaming concepts in several domain names (e.g., community administration, monetary analytics). eventually, the amount concludes with an summary of present facts streaming items and new software domain names (e.g. cloud computing, enormous info analytics, and intricate occasion processing), and a dialogue of destiny instructions during this interesting field.
The e-book offers a accomplished evaluate of middle techniques and technological foundations, in addition to a number of platforms and functions, and is of specific curiosity to scholars, academics and researchers within the zone of knowledge circulate administration.
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Additional resources for Data Stream Management: Processing High-Speed Data Streams
S. Vitter, Random sampling with a reservoir. ACM Trans. Math. Softw. 11, 37–57 (1985) 55. S. Vitter, Faster methods for random sampling. Commun. ACM 27, 703–718 (1984) 56. T. Chao, A general purpose unequal probability sampling plan. Biometrika 69, 653–656 (1982) 57. H. Brönnimann, B. Chen, M. J. Haas, Y. Qiao, P. Scheuermann, Efficient data reduction methods for on-line association rule discovery, in Data Mining: Next Generation Challenges and Future Directions, ed. by H. Kargupta, A. Joshi, K.
R. Gemulla, W. J. Haas, Maintaining bounded-size sample synopses of evolving datasets. VLDB J. 17(2), 173–202 (2008) 62. R. Gemulla, W. J. Haas, Maintaining Bernoulli samples over evolving multisets, in Proc. Twenty Sixth PODS (2007), pp. J. Haas 63. G. Cormode, S. Muthukrishnan, I. Rozenbaum, Summarizing and mining inverse distributions on data streams via dynamic inverse sampling, in Proc. 31st VLDB (2005), pp. 25–36 64. G. Frahling, P. Indyk, C. Sohler, Sampling in dynamic data streams and applications, in Proc.
G. assume that zi corresponds to some element xr in Q . Let ys be the largest element in Q that is not larger than xr (ys is undefined if there is no such element), and let yt be the smallest element in Q that is not smaller than xr (yt is undefined if there is no such element). Then if ys undefined; otherwise, rminQ (zi ) = rminQ (xr ) rminQ (xr ) + rminQ (ys ) rmaxQ (zi ) = rmaxQ (xr ) + rmaxQ (ys ) rmaxQ (xr ) + rmaxQ (yt ) − 1 if yt undefined; otherwise. Lemma 1 Let Q be an -approximate quantile summary for a multiset S , and let Q be an -approximate quantile summary for a multiset S .