By Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche

This ebook constitutes the completely refereed complaints of the nineteenth East eu convention on Advances in Databases and knowledge structures, ADBIS 2015, held in Poitiers, France, in September 2015. The 31 complete papers and 18 brief papers offered have been rigorously chosen and reviewed from a hundred thirty five submissions. The papers are geared up in topical sections similar to database thought and entry tools; person necessities and database evolution; multidimensional modeling and OLAP; ETL; transformation, extraction and archiving; modeling and ontologies; time sequence processing; functionality and tuning; complicated question processing; approximation and skyline; confidentiality and trust.

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Additional info for Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings

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The graphs show that our techniques lead to notable gains when compared to the original Slim-tree. g. 93 % in disk accesses. When the selectivity was 50 or more, the gains were less expressive. Nevertheless, our techniques consistently outperformed the original structure, regarding every dataset, in execution time, number of distance calculations and number of disk accesses. The first row of graphs in Fig. 3 corresponds to results over ALOI-T. 83 % in disk accesses. 55 % to 82 %, respectively for the same variables.

Since there are many more leaf nodes than index nodes, storing less information within leaf nodes leads to a little smaller file sizes. 32 % higher, as expected, because it involves additional computations such as distance calculations regarding the cutting local additional pivots. 528s. Thus, considering the performance gain in queries, the proposed techniques are worth the higher building time. Table 1. H. Oliveira et al. Fig. 4. Results of the scalability experiments over CoPhIR Finally, we evaluated how the gain promoted by CLAP and ACIR techniques behaves according to the dataset size.

In general, as expected, efficiency of MineCDD improves for high k vales. (b). Influence of ε:– Next, we show how ε affects the efficiency of MineCDD. Figure 3 (c) presents a plot of runtime vs. k for different ε values. For lack of space, we present only the results on the Mammographic data set. The runtime of MineCDD increases for high ε values – more DFs are considered for the LHS cDFs, increasing the search space of CDDs. This is shown in the graph. 3. 3 is best for intr(σ)) on pruning the minimal cover of CDDs.

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