Computing a k-route over uncertain geographical data

Eliyahu Safra*, Yaron Kanza, Nir Dolev, Yehoshua Sagiv, Yerach Doytsher

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

Abstract

An uncertain geo-spatial dataset is a collection of geo-spatial objects that do not represent accurately real-world entities. Each object has a confidence value indicating how likely it is for the object to be correct. Uncertain data can be the result of operations such as imprecise integration, incorrect update or inexact querying. A k-route, over an uncertain geo-spatial dataset, is a path that travels through the geo-spatial objects, starting at a given location and stopping after visiting k correct objects. A k-route is considered shortest if the expected length of the route is less than or equal to the expected length of any other k-route that starts at the given location. This paper introduces the problem of finding a shortest k-route over an uncertain dataset. Since the problem is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Three methods for computing a short k-route are presented. The three methods are compared analytically and experimentally. For these three methods, experiments on both synthetic and realworld data show the tradeoff between the quality of the result (i.e., the expected length of the returned route) and the efficiency of the computation.

Original languageEnglish
Title of host publicationAdvances in Spatial and Temporal Databases - 10th International Symposium, SSTD 2007, Proceedings
PublisherSpringer Verlag
Pages276-293
Number of pages18
ISBN (Print)9783540735397
DOIs
StatePublished - 2007
Event10th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2007 - Boston, MA, United States
Duration: 16 Jul 200718 Jul 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4605 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th International Symposium on Advances in Spatial and Temporal Databases, SSTD 2007
Country/TerritoryUnited States
CityBoston, MA
Period16/07/0718/07/07

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