By Lukasz Golab, Nesime Tatbul, M. Tamer Ozsu

ISBN-10: 1608452727

ISBN-13: 9781608452729

During this lecture many purposes procedure excessive volumes of streaming information, between them web site visitors research, monetary tickers, and transaction log mining. quite often, a knowledge circulation is an unbounded facts set that's produced incrementally over the years, instead of being to be had in complete ahead of its processing starts. during this lecture, we provide an summary of modern study in circulation processing, starting from answering basic queries on high-speed streams to loading real-time info feeds right into a streaming warehouse for off-line research. we'll speak about kinds of structures for end-to-end move processing: info movement administration platforms (DSMSs) and Streaming facts Warehouses (SDWs). a standard database administration procedure commonly procedures a movement of ad-hoc queries over particularly static information. against this, a DSMS evaluates static (long-running) queries on streaming info, creating a unmarried omit the information and utilizing constrained operating reminiscence. within the first a part of this lecture, we are going to talk about examine difficulties in DSMSs, reminiscent of non-stop question languages, non-blocking question operators that consistently react to new facts, and non-stop question optimization. the second one half covers SDWs, which mix the real-time reaction of a DSMS by way of loading new info once they come with a knowledge warehouse's skill to regulate Terabytes of ancient info on secondary garage. desk of Contents: creation / info circulate administration platforms / Streaming info Warehouses / Conclusions

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Widom. Resource sharing in continuous sliding-window aggregates. In Proc. 30th Int. Conf. on Very Large Data Bases, pages 336–347, 2004. 31 A. Arasu, B. Babcock, S. Babu, J. McAlister, and J. Widom. Characterizing memory requirements for queries over continuous data streams. ACM Trans. , 29(1):162–194, March 2004a. 974756 29 A. Arasu, M. Cherniack, E. Galvez, D. Maier, A. S. Maskey, E. Ryvkina, M. Stonebraker, and R. Tibbets. Linear road: a stream data management benchmark. In Proc. 30th Int. Conf.

Feb. 1 Feb. 2 Update – Feb. 2. 3: Identifying out-of-date partitions of rolled-up tables in response to an update to a fact table. The challenge in efficient update propagation is to identify which partitions of a derived table may have changed after a base table has been updated. In some cases, we can use dimension hierarchies to find partitions that need updating. 3 shows that if daily sales data are added to a fact table, then we can determine which partitions of the weekly and monthly roll-up tables are out-of-date.

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Data Stream Management by Lukasz Golab, Nesime Tatbul, M. Tamer Ozsu


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