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 knowledge platforms, ADBIS 2013, held in Genoa, Italy, in September 2013. The 26 revised complete papers offered including 3 invited papers have been rigorously chosen and reviewed from ninety two submissions. The papers are prepared in topical sections on ontologies; indexing; facts mining; OLAP; XML info processing; querying; similarity seek; GPU; querying in parallel architectures; functionality evaluate; disbursed architectures.
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Additional info for Advances in Databases and Information Systems
In , the authors proposed a method that stores star neighborhoods in a tree structure. Modified procedure of generating candidates results in construction of proper collocation instances only. Due to the complexity of the spatial data, internal data structures used by collocation discovery algorithms [4, 6, 7, 9, 11, 12] can necessitate large amounts of the system memory. Nanopoulos et al. consider in their work  real world database systems, where OLTP and data mining queries for association rules discovery are executed simultaneously.
With increasing threshold for the neighborhood size (1 unit is approx. 111 meters), the number of ordered neighbor relationship sets rises dramatically. Presented results were gathered for two memory thresholds: 40% and 80% of the required size. The PMiCPI-trees method is more efficient than competing algorithm and is much less sensitive to the size of the required memory. The conducted experiments show that in environments with a limited memory our new method performs much better than the original solutions.
Figure 5(d) and 5(e) present how disk is accessed when there is a lack of memory to store whole neighborhoods. The high number of disk I/O calls (Fig. 5(d)) for the MiCPI-tree algorithm arises from multiple searches performed on disk (also for non-existing elements). The disk I/O transfer (Fig. 5(e)) is similar for both methods however it increases more rapidly with decreasing cache size in the MiCPI-tree method. Finally, in Fig. 5(f), we present how the performance changes with the density of the data.