Abstract
AbstractWe provide a critical state of the art survey of linguistic data summarization in its fuzzy logic based version, meant as a process for a comprehensive description of big and complex data sets via short statements in natural language. These statements are represented by protoforms in the form of linguistically quantified propositions dealt with using tools and techniques of fuzzy logic to grasp an inherent imprecision of natural language that is very difficult, if not impossible, for traditional natural language generation related approaches to linguistic summarization. Such linguistic data summaries can provide a human user, whose only natural means of articulation and communication is natural language, with a simple yet effective and efficient means for the representation and manipulation of knowledge about processes and systems. We concentrate on the linguistic summarization of dynamic processes and systems, dealing with data represented as time series. We extend the basic, static data oriented concept of a linguistic data summary to the case of time series data, present various possible protoforms of linguistic summaries, and an analysis of their properties and ways of generation. We show two our own real applications of the new tools of linguistic summarization of time series, for the summarization of quotations of an investment (mutual) fund, and of Web server logs, to show the power of the tool. We also mention some other applications known from the literature. We conclude with some remarks on the strength of the linguistic summarization for broadly perceived data mining and knowledge discovery, emphasize its potentials, and outline some possible further research directions, being strongly convinced that the fuzzy logic based approach to linguistic summarization of time series is one of more important areas in which fuzzy logic can play a crucial role in the years to come.KeywordsLinguistic summarizationNatural languageFuzzy logicLinguistic quantifiersData miningKnowledge discoveryBig data sets
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