Download Data Warehousing and Knowledge Discovery: 15th International by Amine Ghrab, Sabri Skhiri, Salim Jouili, Esteban Zimányi PDF

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

This publication 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 provided have been rigorously reviewed and chosen from 89 submissions. The papers are geared up in topical sections on modeling and ETL, question optimization and parallelism, spatial info warehouses and functions, textual content mining and OLAP, advice and prediction, info mining optimization and computing device studying ideas, mining and processing facts streams, clustering and knowledge mining functions, social community and graph mining, and occasion series and internet mining.

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

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CheckStat to be applied earlier and Currency to be applied at the very end to decrease the number of total currency transformations. Declarative Expression and Optimization of Data-Intensive Flows 17 Fig. 2. A more complex ETL flow More Complex Flows. Fig. 2 shows a more complex flow on top of two real data sources, also taken from [18]. The tasks employed are: (i) NotNull, which checks the field Cost for NULL values, so that such values are replaced or the tuple is dropped. (ii) dollar2euro, which changes the values in Cost from dollars to euros.

32 P. C. Davis Web Table 1: Web Table 2: S. No. President’s Name President’s Name Birth Date From-To Vice President Terms Served Birth Place Fig. 4. Example Partial Schema 1 Web Table A: Web Table B: Web Table C: State # Capital President Population Date of Birth President’s Name Vice President From-To Fig. 5. Example Partial Schema 2 Final Table 1: Final Table 2: Final Table 3: S. No. President’s Name President’s Name State Capital From-To Birth Date Vice President Terms Served Birth Place Population Fig.

T1i is the wall time of job i in the original version, and t2i is that in the uncoupled version. Use toriginal = max({t1i }) and tuncoupled = max({t2i }) (1 ≤ i ≤ N ) to represent the total wall time in the original version and the uncoupled version. Then we define the throughput increment rate as I: I= Tuncoupled − Toriginal = Toriginal Nuncoupled Noriginal tuncoupled − toriginal Noriginal toriginal = toriginal −1 tuncoupled (7) Then we define the rate of total wall time reduction as r, and the rate of job i’s wall time reduction as ri : r= toriginal − tuncoupled toriginal (8) t1i − t2i t1i (9) ri = The impact factor of job i, λi , represents the proportion of job i’s wall time in all the jobs.

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