Advances in Databases and Information Systems by Barbara Catania, Giovanna Guerrini, Jaroslav Pokorny

By Barbara Catania, Giovanna Guerrini, Jaroslav Pokorny

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This booklet constitutes the completely refereed lawsuits of the seventeenth East-European convention on Advances in Databases and data structures, ADBIS 2013, held in Genoa, Italy, in September 2013. The 26 revised complete papers offered including 3 invited papers have been conscientiously chosen and reviewed from ninety two submissions. The papers are equipped in topical sections on ontologies; indexing; facts mining; OLAP; XML facts processing; querying; similarity seek; GPU; querying in parallel architectures; functionality overview; allotted architectures.

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Zakrzewicz 5 Experimental Evaluation We conducted a series of experiments using both synthetic and real world1 datasets. Hardware: Linux workstation equipped with 1800MHz Athlon CPU, 2GB RAM and 120GB 7200RPM HDD. Parameters for experiments on the synthetic data: the number of spatial features [15-50], the minimum prevalence threshold: [25%40%], and the neighbor distance [10-20]. 55]. We have examined the performance of MiCPI-tree and PMiCPI-trees algorithms. In the first series of the experiments (Fig.

Proposed a tree structure to store neighbor relationships and identify collocation instances recursively from it. Compared with the join-less method [12], this approach eliminates an additional, computationally demanding filtering step required to check whether a particular instance holds the clique definition. However, this algorithm gives up the Apriori-like [1] strategy for generating new candidates, which in many cases can significantly reduce the number of candidates and therefore increase the overall performance.

Collocation Pattern Mining in Limited Memory Environment Using Materialized iCPI-tree. , Dayal, U. ) Proceedings of the 14th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2012). LNCS. Springer (accepted to publish, 2012) 4. : Discovering Co-location Patterns from Spatial Datasets: A General Approach. IEEE Transactions on Knowledge and Data Engineering 16, 472–485 (2004) 5. : Memory-Adative Association Rules Mining. Inf. Systems 29(5), 365–384 (2004) 6. : Discovering Spatial Co-location Patterns: A Summary of Results.

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