Efficient schemes for similarity-aware refinement of aggregation queries

Albarrak, Abdullah M. and Sharaf, Mohamed A. (2017) Efficient schemes for similarity-aware refinement of aggregation queries. World Wide Web, 1-31. doi:10.1007/s11280-017-0434-4

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Author Albarrak, Abdullah M.
Sharaf, Mohamed A.
Title Efficient schemes for similarity-aware refinement of aggregation queries
Journal name World Wide Web   Check publisher's open access policy
ISSN 1386-145X
Publication date 2017-01-23
Year available 2017
Sub-type Article (original research)
DOI 10.1007/s11280-017-0434-4
Open Access Status File (Author Post-print)
Start page 1
End page 31
Total pages 31
Place of publication New York, NY United States
Publisher Springer New York
Language eng
Formatted abstract
Interactive data exploration platforms in Web, business and scientific domains are becoming increasingly popular. Typically, users without prior knowledge of data interact with these platforms in an exploratory manner hoping they might retrieve the results they are looking for. One way to explore large-volume data is by posing aggregate queries which group values of multiple rows by an aggregate operator to form a single value: an aggregated value. Though, when a query fails, i.e., returns undesired aggregated value, users will have to undertake a frustrating trial-and-error process to refine their queries, until a desired result is attained. This data exploration process, however, is growing rather difficult as the underlying data is typically of large-volume and high-dimensionality. While heuristic-based techniques are fairly successful in generating refined queries that meet specified requirements on the aggregated values, they are rather oblivious to the (dis)similarity between the input query and its corresponding refined version. Meanwhile, enforcing a similarity-aware query refinement is rather a non-trivial challenge, as it requires a careful examination of the query space while maintaining a low processing cost. To address this challenge, we propose an innovative scheme for efficient Similarity-Aware Refinement of Aggregation Queries called (EAGER) which aims to balance the tradeoff between satisfying the aggregate and similarity constraints imposed on the refined query to maximize its overall benefit to the user. To achieve that goal, EAGER implements efficient strategies to minimize the costs incurred in exploring the available search space by utilizing similarity-based and monotonic-based pruning techniques to bound the search space and quickly find a refined query that meets users’ expectations. Our extensive experiments show the scalability exhibited by EAGER under various workload settings, and the significant benefits it provides.
Keyword Query refinement
Data exploration
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
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Created: Tue, 14 Feb 2017, 00:28:26 EST by System User on behalf of School of Information Technol and Elec Engineering