Using Stochastic Automaton for Data Consolidation

Olexandr V. Koval, Valeriy O. Kuzminykh, Dmitriy V. Khaustov


Background. Development of methods and algorithms for efficient search of relevant information on demand. The article deals with the consolidation of data for subsequent use in the information and analytical systems.

Objective. The aim of the paper is to identify capabilities and build relevant information search algorithms from disparate sources by analyzing the probability information identifying the possible presence of relevant documents in these sources.

Methods. To find the relevant information for search queries the approach based on the use of probability estimates of relevant documents available in the sources of further increasing the number of selected documents from these sources to analyze their relevance to the query is used.

Results. A stochastic programmable automaton structure to ensure selection of the most possible information sources by relevance parameters and information retrieval algorithm based on the use of stochastic automaton were developed.

Conclusions. The described algorithm using stochastic automaton for data consolidation allows developing a set of software tools, provides plenty full and holistic data consolidation problem-solving for diverse systems which search for information from information sources different in composition and presentation type.


Open data sources; Data consolidation; Information-analytical systems; Information retrieval systems; Probabilistic models; Relevance; Big data tasks

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