New PDF release: Database and Expert Systems Applications: 19th International

By Colette Rolland (auth.), Sourav S. Bhowmick, Josef Küng, Roland Wagner (eds.)

ISBN-10: 3540856536

ISBN-13: 9783540856535

ISBN-10: 3540856544

ISBN-13: 9783540856542

This ebook constitutes the refereed court cases of the nineteenth overseas convention on Database and specialist platforms functions, DEXA 2008, held in Turin, Italy, in September 2008.

The seventy four revised complete papers offered including 1 invited paper have been conscientiously reviewed and chosen from 208 submissions. The papers are prepared in topical sections on info privateness; temporal, spatial and excessive dimensional databases; semantic internet and ontologies; question processing; internet and knowledge retrieval; cellular info and data; information and data streams; facts mining algorithms; multimedia databases; facts mining structures, info warehousing, OLAP; info and data semantics; XML databases; functions of database, details, and determination aid structures; and schema, procedure and information modelling and evolution.

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Extra resources for Database and Expert Systems Applications: 19th International Conference, DEXA 2008, Turin, Italy, September 1-5, 2008. Proceedings

Example text

And ei [S] denotes the multi-set of ei ’s SA values. 2 The Information Loss Metric In fact, our BSGI algorithm does not rely on a certain information loss metric. Any metric that captures the quality of generalization[12,15,18] can be adopt by the algorithm. In our experiment, we use the metric proposed by [12], denoted as IL metric. 22 Y. Ye et al. IL metric defines the information loss for categorical and numerical attributes separately. The information loss of a tuple is defined by summing up the loss of all attributes(multiplied by different weights).

We first partition the support range in database into the bin number of intervals. Each interval has the same number of items so that each bin, Bi , contains every items in the ith interval. Next, the support constraints with the schemas which are made up of all possible combinations of bins were generated. And the support threshold of the support constraint SCk (B1 , . . , Br ) ≤ θk is defined as follows: θ = min{γ k−1 × S(Bi ) × . . × S(Br ), 1} where S(Bi ) denotes the smallest item support for the Bi , and γ is an integer larger than 1.

This cost is worthwhile, because Unique Distinct l-diversity largely enhances privacy preservation. In sum, the excessively long execution time and high information loss render Incognito almost impractical. BSGI achieves the optimal DM and AverageSize metric. With regard to the IL metric, BSGI still outperforms M ondrian apparently. The BSGI is an highly efficient algorithm with low information loss. In the meantime, it achieves the stronger Unique Distinct l-diversity model, which preserves privacy excellently.

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Database and Expert Systems Applications: 19th International Conference, DEXA 2008, Turin, Italy, September 1-5, 2008. Proceedings by Colette Rolland (auth.), Sourav S. Bhowmick, Josef Küng, Roland Wagner (eds.)

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