Database and Expert Systems Applications: 23rd International by Cristhian A. D. Deagustini, Santiago E. Fulladoza Dalibón,

By Cristhian A. D. Deagustini, Santiago E. Fulladoza Dalibón, Sebastián Gottifredi (auth.), Stephen W. Liddle, Klaus-Dieter Schewe, A Min Tjoa, Xiaofang Zhou (eds.)

This quantity set LNCS 7446 and LNCS 7447 constitutes the refereed complaints of the twenty third overseas convention on Database and specialist structures functions, DEXA 2012, held in Vienna, Austria, September 3-6, 2012.
The forty nine revised complete papers provided including 37 brief papers and a couple of keynote talks have been rigorously reviewed and chosen from 179 submissions. those papers speak about a number subject matters together with: database question processing, particularly XML queries; labelling of XML records; computational potency, info extraction; personalization, personal tastes, and score; defense and privateness; database schema evaluate and evolution; semantic internet; privateness and provenance; facts mining; information streaming; disbursed structures; looking out and question answering; structuring, compression and optimization; failure, fault research, and uncertainty; predication, extraction, and annotation; rating and personalisation; database partitioning and function dimension; advice and prediction structures; enterprise strategies; social networking.

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Extra info for Database and Expert Systems Applications: 23rd International Conference, DEXA 2012, Vienna, Austria, September 3-6, 2012. Proceedings, Part II

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Unfortunately, the bitmap index created is sensitive to the distribution and cardinality of the input data, taking anywhere from 30 to 300% of the raw column size. The space can partly be reduced through techniques such as precision binning, at the cost of disturbing the distribution of values along the bins. Analytics-Driven Lossless Data Compression for Rapid In-situ Indexing 19 On the other side of the coin, data compression methods within databases have been widely studied [9, 12, 18]. For example, the column-oriented database C-Store [2] uses null compression (elimination of zeroes), dictionary encoding, and run-length encoding for effective data reduction of attributes organized contiguously, as opposed to the traditional row-store organization.

1861, pp. 942–956. Springer, Heidelberg (2000) 11. : Consistent query answering in databases. SIGMOD Record 35(2), 68–76 (2006) 12. : A defeasible logic reasoner for the semantic web. Int. J. Semantic Web Inf. Syst. 2(1), 1–41 (2006) 13. : Introduction to Inconsistency Tolerance. , Schaub, T. ) Inconsistency Tolerance. LNCS, vol. 3300, pp. 1–14. Springer, Heidelberg (2005) Analytics-Driven Lossless Data Compression for Rapid In-situ Indexing, Storing, and Querying John Jenkins1,2 , Isha Arkatkar1,2, Sriram Lakshminarasimhan1,2, Neil Shah1,2, Eric R.

2 is, as will be shown, effective at improving the compression ratio of many scientific datasets, but is not optimized for query processing. If a range query is performed using our compression index, the entire RID-to-bin mapping M would need to be traversed to map the binned data back to RIDs. Thus, at the cost of additional storage, we optimize for range queries by using an inverted index M −1 which maps each bin to a list of RIDs sharing the same high-order bytes, creating a bin-based value-to-RID mapping.

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