Studia Informatica


Volume: 1-2(21)
Year: 2017
Publishing: Publishing House of University of Natural Science
Scientific Editor: Wojciech Penczek,
Review Board: PDF File
ISSN: 1731-2264

Contents

Abramovich M., Mitskevich M.,
Statistical methods and algorithms for spatio-temporal cluster analysis
pp. 5-14
Abstract: The global clusterization test and scan statistic method for studying geographical distribution of the objects are considered. The algorithm of windows set construction for the flexible spatial was developed. The robust version of spatial scan statistic method is proposed. The children carcinoma of the Belarus was analyzed using scan statistic method.
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Barczak A., Zacharczuk D., Pluta D.,
Methods of optimization of distributed databases in oracle – part 2
pp. 15-46
Abstract: The second part of the paper devoted to optimization of distributed databases. This part presents tests which confirm the efficiency of database tuning methods, described in part one. Analysis of tests results based on the developed database is presented.
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Novikov S.,
Parallelization of computations for generating combinations
pp. 47-62
Abstract: An effective sequential algorithm and two parallel algorithms for generating combinations without repetitions of m out n of objects, represented by Boolean vectors, are proposed. One of them allows one to calculate starting and ending combinations for the subset, generated by each computing processor. The second algorithm firstly generates short (m-component) vectors on several computing processors. After that, by using special [n/m]-component vectors, it connects the short vectors into ncomponent Boolean vectors, each of which containing of exactly m units.
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Ruciński D.,
The neural modelling in chosen task of Electric Power Stock Market
pp. 63-83
Abstract: The work contains selected results of the neural modelling for the Electric Power Exchange (EPE) for the Day Ahead Market (DAM). The paper contains description of the neural modelling method, the way of preparing (pre-processing) data used for leaning of Artificial Neural Network (ANN), description of achieved neural models of EPE, the comparative study results and the sensitivity study results. The results which was obtained was interpreted and discussed in the systemic category.
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