Refinement driven processing of aggregation Constrained queries

Abstract

© 2016, Copyright is with the authors. Although existing database systems provide users an efficient means to select tuples based on attribute criteria, they however provide little means to select tuples based on whether they meet aggregate requirements. For instance, a requirement may be that the cardinality of the query result must be 1000 or the sum of a particular attribute must be < $5000. In this work, we term such queries as "Aggregation Constrained Queries" (ACQs). Aggregation constrained queries are crucial in many decision support applications to maintain a product's competitive edge in this fast moving field of data processing. The challenge in processing ACQs is the unfamiliarity of the underlying data that results in queries being either too strict or too broad. Due to the lack of support of ACQs, users have to resort to a frustrating trial-and-error query refinement process. In this paper, we introduce and define the semantics of ACQs. We propose a refinement-based approach, called ACQUIRE, to efficiently process a range of ACQs. Lastly, in our experimental analysis we demonstrate the superiority of our technique over extensions of existing algorithms. More specifically, ACQUIRE runs up to 2 orders of magnitude faster than compared techniques while producing a 2X reduction in the amount of refinement made to the input queries.