Refinement driven processing of aggregation Constrained queries
Name
paper-23.pdf
Description
Published version
Size
1.1 MB
Format
Adobe PDF
Checksum (MD5)
0c47f030367b0e40455f2930b1d3b816
Author(s)
Vartak, M
Raghavan, V
Rundensteiner, E
Madden, S
Date Issued
January 1, 2016
Journal
Advances in Database Technology - EDBT
Citation
Vartak, M, Raghavan, V, Rundensteiner, E and Madden, S. 2016. "Refinement driven processing of aggregation Constrained queries." Advances in Database Technology - EDBT, 2016-March.
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.5441/002/edbt.2016.12
