Efficient processing of neighboring skyline queries with consideration of distance, quality, and cost

Currently, many of the processing techniques for the location - based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of...

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Published in:	Computing Vol. 102; no. 2; pp. 523 - 550
Main Author:	Huang, Yuan-Ko
Format:	Journal Article
Language:	English
Published:	Vienna Springer Vienna 01.02.2020 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Information Systems Applications (incl.Internet) Queries Query processing Software Engineering Neighboring skyline set Pruning criteria 68W40 68W01 Different types of objects - Location-based queries 65D18
ISSN:	0010-485X, 1436-5057
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Abstract	Currently, many of the processing techniques for the location - based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set ). Three new types of location-based queries, the Distance - based neighboring skyline query ( Dist - NS query ), the Cost - based neighboring skyline query ( Cost - NS query ), and the Budget - based neighboring skyline query ( BGT - NS query ), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the R a , c - tree , is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the R a , c - trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.
AbstractList	Currently, many of the processing techniques for the location - based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set ). Three new types of location-based queries, the Distance - based neighboring skyline query ( Dist - NS query ), the Cost - based neighboring skyline query ( Cost - NS query ), and the Budget - based neighboring skyline query ( BGT - NS query ), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the R a , c - tree , is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the R a , c - trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms. Currently, many of the processing techniques for the location-based queries provide information of a single type of spatial objects, based on their spatial closeness to the query object. However, in real-life applications the user may be interested in obtaining information about different types of objects, in terms of their quality, cost, and neighboring relationship. We term the different types of objects with better quality and closer to each other the Neighboring skyline set (or NS set). Three new types of location-based queries, the Distance-based neighboring skyline query (Dist-NS query), the Cost-based neighboring skyline query (Cost-NS query), and the Budget-based neighboring skyline query (BGT-NS query), are presented to determine the NS sets according to user’s specific requirement. A R-tree-based index, the Ra,c-tree, is first designed to manage each type of objects with their locations, attributes, and costs. Then, a simultaneous traversal of the Ra,c-trees built on different types of objects is employed with several pruning criteria to prune the non-qualifying object sets as early as possible, so as to improve the query performance. Extensive experiments using the synthetic dataset demonstrate the efficiency and the effectiveness of the proposed algorithms.
Author	Huang, Yuan-Ko
Author_xml	– sequence: 1 givenname: Yuan-Ko orcidid: 0000-0002-6061-3285 surname: Huang fullname: Huang, Yuan-Ko email: huangyk@nkust.edu.tw organization: Department of Maritime Information and Technology, National Kaohsiung University of Science and Technology
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Cites_doi	10.1109/TKDE.2011.229 10.1109/ACCESS.2017.2688472 10.1145/170036.170075 10.1145/276305.276326 10.1007/s00778-005-0166-4 10.1109/TKDE.2006.185 10.1145/503099.503101 10.1109/TKDE.2006.15 10.1145/568271.223794 10.1016/j.is.2012.02.003 10.1145/320248.320255 10.1145/322092.322095 10.1109/ICDE.2001.914855 10.1145/342009.335415 10.1109/ICDE.2003.1260846 10.1016/B978-155860869-6/50032-9 10.1145/2484838.2484866 10.1016/B978-012722442-8/50052-5 10.1109/ICDE.2006.104 10.1145/1007568.1007638 10.1145/872757.872814 10.1109/ICDE.2007.368964 10.1109/ICDE.2007.367925 10.1145/564691.564730
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SubjectTerms	Algorithms Artificial Intelligence Computer Appl. in Administrative Data Processing Computer Communication Networks Computer Science Information Systems Applications (incl.Internet) Queries Query processing Software Engineering
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Title	Efficient processing of neighboring skyline queries with consideration of distance, quality, and cost
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