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Journal Articles Journal of Intelligent Information Systems Year : 2018

HIFUN - a high level functional query language for big data analytics

Abstract

We present a high level query language, called HIFUN, for defining analytic queries over big datasets, independently of how these queries are evaluated. An analytic query in HIFUN is defined to be a well-formed expression of a functional algebra that we define in the paper. The operations of this algebra combine functions to create HIFUN queries in much the same way as the operations of the relational algebra combine relations to create algebraic queries. The contributions of this paper are: (a) the definition of a formal framework in which to study analytic queries in the abstract; (b) the encoding of a HIFUN query either as a MapReduce job or as an SQL group-by query; and (c) the definition of a formal method for rewriting HIFUN queries and, as a case study, its application to the rewriting of MapReduce jobs and of SQL group-by queries. We emphasize that, although theoretical in nature, our work uses only basic and well known mathematical concepts, namely functions and their basic operations.
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hal-04467615 , version 1 (20-02-2024)

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Nicolas Spyratos, Tsuyoshi Sugibuchi. HIFUN - a high level functional query language for big data analytics. Journal of Intelligent Information Systems, 2018, 51 (3), pp.529-555. ⟨10.1007/s10844-018-0495-6⟩. ⟨hal-04467615⟩
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