By Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche
This publication constitutes the completely refereed complaints of the nineteenth East eu convention on Advances in Databases and data platforms, ADBIS 2015, held in Poitiers, France, in September 2015. The 31 complete papers and 18 brief papers awarded have been conscientiously chosen and reviewed from one hundred thirty five submissions. The papers are geared up in topical sections equivalent to database thought and entry tools; consumer requisites 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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Extra resources for Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings
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 this experiment, we ﬁxed k to 10 and varied the dataset size from 10k to 10M elements with 282 dimensions, using the Weighted L2 metric and random query elements. Figure 4 presents the obtained results. It can be seen that the improvement raises with the dataset size regarding distance calculations and execution time. The gain in disk accesses drops until 1M elements and afterwards presents a sensible increase, being always positive.
Several applications handle online complex data and, consequently, demand eﬃcient dynamic indexes to be successful. In this context, this work presents two techniques for improving the pruning ability of dynamic MAMs: (i) using cutting local additional pivots to reduce distance calculations and (ii) anticipating information from child nodes to reduce unnecessary disk accesses. The experiments reveal signiﬁcant improvements in a dynamic MAM, reducing execution time in more than 50 % for similarity queries posed on datasets ranging from moderate to high dimensionality and cardinality.
Given an integer value k ≥ 1, the k-Nearest Neighbor query (k-NNq) retrieves k tuples ti from relation R with values si for attribute Sj that have the lowest distance from sq according to δ. An example of k-Nearest Neighbors query is: “Select the 4 most similar images to the image Q”. 2 Metric Access Methods There are two main categories of access methods for indexing complex data. The ﬁrst category is a class of access methods that support data domains represented in dimensional spaces, especially spatial data, known as Spatial Access Methods (SAMs) .
Advances in Databases and Information Systems: 19th East European Conference, ADBIS 2015, Poitiers, France, September 8-11, 2015, Proceedings by Morzy Tadeusz, Patrick Valduriez, Ladjel Bellatreche
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