Abstract

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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.