An Efficient Technique for Searching Very Large Files with Fuzzy Criteria Using the Pigeonhole Principle

Abstract

Big Data is the new term of the exponential growth of data in the Internet. The importance of Big Data is not about how large it is, but about what information you can get from analyzing these data. Such analysis would help many businesses on making smarter decisions, and provide time and cost reduction. Therefore, to make such analysis, you will definitely need to search the large files on Big Data. Big Data is such a construction where sequential search is prohibitively inefficient, in terms of time and energy. Therefore, any new technique that allows very efficient search in very large files is highly demanded. The paper presents an innovative approach for efficient searching with fuzzy criteria in very large information systems (Big Data). Organization of efficient access to a large amount of information by an "approximate" or "fuzzy" indication is a rather complicated Computer Science problem. Usually, the solution of this problem relies on a brute force approach, which results in sequential look-up of the file. In many cases, this substantially undermines system performance. The suggested technique in this paper uses different approach based on the Pigeonhole Principle. It searches binary strings that match the given request approximately. It substantially reduces the sequential search operations and works extremely efficiently from several orders of magnitude including speed, cost and energy. This paper presents a complex developed scheme for the suggested approach using a new data structure, called FuzzyFind Dictionary. The developed scheme provides more accuracy than the basic utilization of the suggested method. It also, works much faster than the sequential search.

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