By Barbara Catania, Giovanna Guerrini, Jaroslav Pokorny
-Fast convention proceedings
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This ebook constitutes the completely refereed complaints 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 provided including 3 invited papers have been rigorously chosen and reviewed from ninety two submissions. The papers are prepared in topical sections on ontologies; indexing; information mining; OLAP; XML facts processing; querying; similarity seek; GPU; querying in parallel architectures; functionality assessment; disbursed architectures.
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In general, a plane sweeps across the space, only to stop at data points (lines 18, 20). An ordered buffer, denoted by B1 , is maintained to store points lying in the strip (called window) of given width (neighbor relationship criterion). In Fig. 5. Partitioning Approach to Collocation Pattern Mining 27 Algorithm 1. The PMiCPI-trees algorithm with the plane sweep strategy Variables: F - set of spatial features S - set of instances dist - max. neighbor distance MEMS - available memory min prev - min.
Boinski and M. Zakrzewicz S contains information about its feature type, instance id and location; (b) a neighbor relationship R over locations; (c) a minimum prevalence threshold (min prev) and minimum conditional probability threshold (min cond prob); (d) a size of the available memory, find efficiently (with respect to the memory constraint) a correct and complete set of collocation rules with participation index ≥ min prev and conditional probability ≥ min cond prob. We assume that relation R is a distance metric based neighbor relationship with a symmetric property and spatial dataset is a point dataset.
The average size of the available memory was equal to 70% of the required size. Additionally, we present results from the original iCPI-tree method. With no special structures to handle limited resources, the performance of the iCPI-tree algorithm decreased substantially. Our new solution performs better than MiCPI-tree for all tested sizes of input datasets. In the second series of experiments we executed collocation mining tasks on the real world dataset. Figure 5(b) (logarithmic scale) presents how the performance of the algorithm changes with the limited memory.