Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66294
Title: Closest interval join using mapreduce
Authors: Zhang, Q
He, A
Liu, C
Lo, E
Keywords: Algorithms
Distributed Systems
Interval Data
MapReduce
Issue Date: 2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, 2016, 7796916, p. 302-311 How to cite?
Abstract: The closest interval join problem is to find all the closest intervals between two interval sets R and S. Applications of closest interval join include bioinformatics and other data science. Interval data can be very large and continue to increase in size due to the advancement of data acquisition technology. In this paper, we present efficient MapReduce algorithms to compute closest interval join. Experiments based on both real and synthetic interval data demonstrated that our algorithms are efficient.
Description: 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, Greece, 17-19 October 2016
URI: http://hdl.handle.net/10397/66294
ISBN: 9781509052066
DOI: 10.1109/DSAA.2016.39
Appears in Collections:Conference Paper

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