Implementation of scalable fuzzy relational operations in MapReduce

One of the main restrictions of relational database models is their lack of support for flexible, imprecise and vague information in data representation and querying. The imprecision is pervasive in human language; hence, modeling imprecision is crucial for any system that stores and processes linguistic data. Fuzzy set theory provides an effective solution to model the imprecision inherent in the meaning of words and propositions drawn from natural language (Zadeh, Inf Control 8(3):338–353, doi: 10.1016/S0019-9958(65)90241-X , 1965 ; IGI Global, https://books.google.com/books?id=nt-WBQAAQBAJ , 2013 ). Several works in the last 20 years have used fuzzy set theory to extend relational database models to permit representation and retrieval of imprecise data. However, to our knowledge, such approaches have not been designed to scale-up to very large datasets. In this paper, the MapReduce framework is used to implement flexible fuzzy queries on a large-scale dataset. We develop MapReduce algorithms to enhance the standard relational operations with fuzzy conditional predicates expressed in natural language.