Conference Proceeding Article
A top-k query shortlists the k records in a dataset that best match the user's preferences. To indicate her preferences, the user typically determines a numeric weight for each data dimension (i.e., attribute). We refer to these weights collectively as the query vector. Based on this vector, each data record is implicitly mapped to a score value (via a weighted sum function). The records with the k largest scores are reported as the result. In this paper we propose an auxiliary feature to standard top-k query processing. Specifically, we compute the maximal locus within which the query vector incurs no change in the current top-k result. In other words, we compute all possible query weight settings that produce exactly the same top-k result as the user's original query. We call this locus the global immutable region (GIR). The GIR can be used as a guide to query vector readjustments, as a sensitivity measure for the top-k result, as well as to enable effective result caching. We develop efficient algorithms for GIR computation, and verify their robustness using a variety of real and synthetic datasets.
Sensitivity analysis, Top-k search, algorithms, Data dimensions, Query vectors, Sensitivity measures, Synthetic datasets, Top-k query processing, User's preferences, Weight setting
Databases and Information Systems | Theory and Algorithms
Data Management and Analytics
SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data: June 22-27, 2014, Snowbird, UT
City or Country
ZHANG, Jilian; MOURATIDIS, Kyriakos; and PANG, Hwee Hwa.
Global Immutable Region Computation. (2014). SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data: June 22-27, 2014, Snowbird, UT. 1151-1162. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2223
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.