Space Filling Approach for Distributed Processing of Top-k Dominating Queries

A top-k dominating query returns k data objects that dominate the highest number of data objects in a given dataset. This query provides us with a set of intuitively preferred data, thus can support a wide variety of multi-criteria decision-making applications, e.g., e-commerce and web search. Due t...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 30; no. 6; pp. 1150 - 1163
Main Authors: Amagata, Daichi, Hara, Takahiro, Onizuka, Makoto
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Approximation algorithms
Cloud computing
Data centers
Decision making
Distributed databases
distributed environments
Distributed processing
Filling
Multiple criterion
Object oriented programming
Query processing
Silicon
Top-k dominating queries
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:A top-k dominating query returns k data objects that dominate the highest number of data objects in a given dataset. This query provides us with a set of intuitively preferred data, thus can support a wide variety of multi-criteria decision-making applications, e.g., e-commerce and web search. Due to the growth of data centers and cloud computing infrastructures, the above applications are increasingly being operated in distributed environments. These motivate us to address the problem of distributed top-k dominating query processing. We propose an efficient decentralized algorithm that exploits virtual points and returns the exact answer. The virtual points are utilized to focus on the data space to be preferentially searched and also to limit the search space to prune unnecessary computation and data forwarding. We also propose two other algorithms, which return an approximate answer set while further reducing query processing time. Extensive experiments on both real and synthetic data demonstrate the efficiency and scalability of our algorithms.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2018.2790387