SAQR: An Efficient Scheme for Similarity-Aware Query Refinement

Albarrak, Abdullah M.; Sharaf, Mohamed A.; Zhou, Xiaofang

doi:10.1007/978-3-319-05810-8_8

Cited by 8 publications

(12 citation statements)

References 14 publications

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“…The input to the refinement process is an initial select-project query I , which is to be transformed into a refined query R. Query I is a conjunctive query defined in terms of d range predicates 2 …”

Section: Query Refinementmentioning

confidence: 99%

“…C R = number of probes made to the evaluation layer (2) In this work, we follow the same approach as in [4,23], in which we consider the probing operation for aggregation estimation as a blackbox and our goal is to minimize the number of such probes. Endeavoring to reduce that incurred cost (i.e., C R ), it has been shown that simple local search techniques based on greedy heuristics (e.g., Hill Climbing) often provide efficient and effective solutions to the aggregate-based query refinement problem, as in [4,13] where the constraint is cardinality.…”

Section: Aggregate-based Query Refinementmentioning

confidence: 99%

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Efficient schemes for similarity-aware refinement of aggregation queries

Albarrak

Sharaf

2017

World Wide Web

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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 similaritybased 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.

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Section: Query Refinementmentioning

confidence: 99%

Section: Aggregate-based Query Refinementmentioning

confidence: 99%

Efficient schemes for similarity-aware refinement of aggregation queries

Albarrak

Sharaf

2017

World Wide Web

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“…SAQR [84] proposed scheme for similarity-aware refinement of aggregate queries, which satisfy the aggregate and similarity constraints imposed on the refined query and maximize its overall utility. The recent research with similarities to our work is [85], however their goal is to generate set of alternative queries to meet the constraint on similarity to the original query and the aggregate constraint.…”

Section: Query Refinementmentioning

confidence: 99%

“…The predicate that is not included in T is equivalent to L i ≤ P i ≤ U i . Similar to [82,84], we convert the range predicated into two single sided predicates. Therefore, l i ≤ P i ≤ u i is converted to two predicates: P i ≤ u i −P i ≤ −l i .…”

Section: Query Refinementmentioning

confidence: 99%

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View recommendation for visual data exploration

Ehsan¹

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The widespread use of digital systems has resulted in an exponential increase in the volume of available data, with data being collected across all domains; from banking and finance to health and energy.However, this data is only beneficial if it is unlocked to generate insights. Visual data exploration plays an important role in this process of discovering insights. Typically, it involves an analyst going through the following steps: 1) selecting a subset of data for analysis, 2) generating different visualizations of Publications during candidature • Humaira Ehsan, Mohamed A. Sharaf, and Panos K. Chrysanthis: Muve: Efficient multi-objective view recommendation for visual data exploration.

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