Read e-book online Data Warehousing and Knowledge Discovery: 15th International PDF

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By Amine Ghrab, Sabri Skhiri, Salim Jouili, Esteban Zimányi (auth.), Ladjel Bellatreche, Mukesh K. Mohania (eds.)

ISBN-10: 3642401309

ISBN-13: 9783642401305

ISBN-10: 3642401317

ISBN-13: 9783642401312

This e-book constitutes the refereed lawsuits of the fifteenth foreign convention on information Warehousing and information Discovery, DaWaK 2013 held in Prague, Czech Republic, in August 2013.
The 24 revised complete papers and eight brief papers awarded have been conscientiously reviewed and chosen from 89 submissions. The papers are equipped in topical sections on modeling and ETL, question optimization and parallelism, spatial info warehouses and purposes, textual content mining and OLAP, advice and prediction, information mining optimization and desktop studying innovations, mining and processing information streams, clustering and information mining functions, social community and graph mining, and occasion series and net mining.

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Read Online or Download Data Warehousing and Knowledge Discovery: 15th International Conference, DaWaK 2013, Prague, Czech Republic, August 26-29, 2013. Proceedings PDF

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Extra resources for Data Warehousing and Knowledge Discovery: 15th International Conference, DaWaK 2013, Prague, Czech Republic, August 26-29, 2013. Proceedings

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Selectivity (seli ): it denotes the average number of returned tuples per input tuple for the i-th service. For filtering operators selectivity is always below 1, for data sources and operators that just manipulate the input, it is exactly one, whereas, for operators that may produce more output records for each input record, the selectivity is above 1. – Input (Ii ): The size of the input of the i-th task in number of tuples per input data tuple. It depends on the product of the selectivities of the preceding tasks in the execution plan.

In: Proc. of the 32nd Int. Conference on Very Large Data Bases VLDB, pp. 355–366 (2006) 18. : Deciding the physical implementation of ETL workflows. In: Proc. of the ACM 10th Int. Workshop on Data Warehousing and OLAP DOLAP, pp. 49–56 (2007) Declarative Expression and Optimization of Data-Intensive Flows 25 19. : Near real time ETL. In: New Trends in Data Warehousing and Data Analysis, pp. 1–31 (2009) 20. : An approach to optimize data processing in business processes. In: VLDB, pp. 615–626 (2007) 21.

The general conclusions drawn is that Greedy and Partition are never the optimal choices, and PGreedy outperforms Swap if less than half of the tasks are selective. Regarding the time needed for the optimizations, even when n = 100, the time for running PGreedy and Partition is approximately a couple of seconds using a machine with an Intel Core i5 660 CPU with 6GB of RAM. Thus it can be safely considered as negligible. 4 Related Work Modern ETL and flow analysis tools, such as Pentaho’s platform2 , do not support declarative statement of flows and automated optimizations of their structure.

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Data Warehousing and Knowledge Discovery: 15th International Conference, DaWaK 2013, Prague, Czech Republic, August 26-29, 2013. Proceedings by Amine Ghrab, Sabri Skhiri, Salim Jouili, Esteban Zimányi (auth.), Ladjel Bellatreche, Mukesh K. Mohania (eds.)


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