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Title: Skyline Queries Constrained by Multi-cost Transportation Networks
Skyline queries are used to find the Pareto optimal solution from datasets containing multi-dimensional data points. In this paper, we propose a new type of skyline queries whose evaluation is constrained by a multi-cost transportation network (MCTN) and whose answers are off the network. This type of skyline queries is useful in many applications. For example, a person wants to find an apartment by considering not only the price and the surrounding area of the apartment, but also the transportation cost, time, and distance between the apartment and his/her work place. Most existing works that evaluate skyline queries on multi-cost networks (MCNs), which are either MCTNs or road networks, find interesting objects that locate on edges of the networks. Formally, our new type of skyline queries takes as input an MCTN, a query point q, and a set of objects of interest D with spatial information, where q and the objects in D are off the network. The answers to such queries are objects in D that are not dominated by other D objects when considering the multiple attributes of these objects and the multiple network cost from q to the solution objects. To evaluate such queries, we propose an exact search algorithm and its improved version by implementing several properties. The space of the exact skyline solutions is huge and can easily reach the order of thousands and incur long evaluation time. We further design much more efficient heuristic methods to find approximate solutions. We run extensive experiments using both real and synthetic datasets to test the effectiveness and efficiency of our proposed approaches. The results show that the exact search algorithm can be dramatically improved by utilizing several properties. The heuristic approaches to find approximate answers can largely reduce the query time and retrieve results that are comparable to the exact solutions.  more » « less
Award ID(s):
1914635 1633330 1757207
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2019 IEEE 35th International Conference on Data Engineering (ICDE)
Page Range / eLocation ID:
926 to 937
Medium: X
Sponsoring Org:
National Science Foundation
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