Enabling Real-Time Business Intelligence: Third by Sunita Sarawagi (auth.), Malu Castellanos, Umeshwar Dayal,

By Sunita Sarawagi (auth.), Malu Castellanos, Umeshwar Dayal, Renée J. Miller (eds.)

In today’s aggressive and hugely dynamic surroundings, corporations want new so- tions to allow them to resolve glossy company difficulties and to make judgements utilizing built-in, reliable, and up to date info. smooth real-time firms have to act on occasions as they take place. they wish new, easy-to-use clever ideas able to interpreting heterogeneous real-time firm info to supply perception and actionable details on the correct time. To let real-time companies, we want basic developments within the technology and engineering that underlie clever details administration together with: the administration of streaming info; the modeling, research and administration of unstructured facts; besides the built-in use of unstructured, se- dependent, and constitution facts. we want new versions and paradigms that bring up the extent of abstraction utilized in such serious applied sciences as ETL, info warehousing, and occasion and enterprise strategy modeling. The sequence of BIRTE workshops goals to supply a discussion board to debate and boost the foundational technological know-how and engineering required to let real-time company intel- gence and the radical functions and suggestions that construct on those foundational te- niques. Following the good fortune of our first workshop, BIRTE 2006, held in Seoul, Korea, together with VLDB 2006, and our moment workshop, BIRTE 2008, held in Auckland, New Zealand, with VLDB 2008, our 3rd workshop used to be held in Lyon, France on August 24, 2009 with VLDB 2009.

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Additional info for Enabling Real-Time Business Intelligence: Third International Workshop, BIRTE 2009, Held at the 35th International Conference on Very Large Databases, VLDB 2009, Lyon, France, August 24, 2009, Revised Selected Papers

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Gupta, and A. Mehta J1 O1 J1 O1 left O3 Segment 1 O3 Segment 2 O3 O2 O2 right J1 Fig. 8. An Simple IDS Case: Binary Join with Two Operator Segments J1 Priority Queue λ1 λ2 left O1 Q1 Q2 O2 buffer right O3 Segment 1 with Priority 1 O3 Segment 2 with Priority 2 J1 Fig. 9. Queuing System Model for Figure 8 When the system fetch a new tuple t to process 1. Check Q1 . If length(Q1 ) > 0, process tuple t from the head of Q1 ; else goto step 3. 2. Check Q2 . If length(Q2 ) == 0, process t1 through pipeline O1 → J1 → O3 ; else process t1 through O1 and put t1 into the buffer.

In: ACM SIGMOD, Providence, RI (June 2009) 15. : Query Processing, Approximation, and Resource Management in a Data Stream Management System. In: CIDR Conference, Asilomar, CA (January 2003) 16. : The CQL Continuous Query Language: Semantic Foundations and Query Execution. VLDB Journal 15(2) (2006) 17. : Towards Real-Time Business Intelligence. BT Technology Journal 23(3) (2005) 18. epx 19. Cognos: IBM Cognos Now! com/products/now/ 20. com/ 21. com/ 22. htm 23. epx 24. org/tpch/ 25. : Linear Road: A Stream Data Management Benchmark.

In: VLDB Conference, Toronto, Canada (September 2004) 26. com Abstract. There are data streams all around us that can be harnessed for tremendous business and personal advantage. For an enterprise-level stream processing system such as CHAOS [1] (Continuous, Heterogeneous Analytic Over Streams), handling of complex query plans with resource constraints is challenging. While several scheduling strategies exist for stream processing, efficient scheduling of complex DAG query plans is still largely unsolved.

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