Abstract
The paper proposes a method for Big data analyzing in the presence of different data sources and different methods of processing these data. The Big data definition is given, the main problems of data mining process are described. The concept of association rules is introduced and the method of association rules searching for working with Big Data is modified. The method of finding dependencies is developed, efficiency and possibility of its parallelization are determined. The developed algorithm makes it possible to assert that the task of detecting association dependencies in distributed databases belongs to the class of P-tasks. The algorithm for finding association dependencies is well-solved with MapReduce. The low asymptotic complexity of the developed association rules mining algorithm and a wide set of data types supported for analysis allow to apply the proposed algorithm in practically all subject areas working with association dependencies in the data domain.
Highlights
Today, various uncoordinated information resources processing is the problem that often arises
Let's define the association rule given as dependence
In order to compare the effectiveness of the developed data analysis method, the three of the most promising methods were selected: Apriori, HybridApriori and the FP-tree method [8, 9, 14]
Summary
Various uncoordinated information resources processing (such as search, system integration, etc.) is the problem that often arises. The processing of various types of uncoordinated data has been carried out by researchers since 1970s. Models and metalanguages for working out different types of data have been developed. Existing models and methods today relate only to pre-known types of data (mostly relational databases or XML data) solving only part of the problem of processing different types of data, for example, indexing to speed up the search. NoSQL databases and availability of mostly semistructural information require new methods and tools for data processing [1]
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