Heuristic algorithms for route-search queries over geographical data

Abstract

In a geographical route search, given search terms, the goal is to find an effective route that (1) starts at a given location, (2) ends at a given location, and (3) travels via geographical entities that are relevant to the given terms. A route is effective if it does not exceed a given distance limit whereas the ranking scores of the visited entities, with respect to the search terms, are maximal. This paper introduces route-search queries, suggests three semantics for such queries and deals with the problem of efficiently answering queries under the different semantics. Since the problem of answering route-search queries 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. Methods for effectively computing routes are presented. The methods are compared analytically and experimentally. For these methods, experiments on both synthetic and real-world data illustrate their efficiency and their effectiveness in computing a route that satisfies the constraints of a route-search query.

Route Search over Probabilistic Geospatial Data

“…A route search over objects that are partitioned into sets has been studied by Kanza et al [7]. However, they considered a semantic of route search that is different from the semantics suggested in this paper.…”

“…The task of computing this initial route is by itself a hard problem, and thus, it is solved heuristically. In our implementation, we used the Infrequent-First Heuristic of [7] for this task.…”

“…In this graph, the x-axis shows the factor by which the length limit is larger than the length of the initial route created by RGeedy. Recall that this initial route has exactly one object from each A i and is constructed by the Infrequent-First Heuristic of [7]. For instance, when x is 1.8, it means that = 1.8 × a, where a is the length of the initial route.…”

Interactive route search in the presence of order constraints

“…Earlier works on route-search queries dealt either with non-probabilistic datasets (e.g., [1,3,7,8,9]), or with the non-interactive version of the problem (e.g., [5,6]). Those results cannot be used to solve the interactive route-search problem that we address in this paper, since they are not designed to take user feedback into account.…”

An interactive approach to route search

“…In earlier work on route search, they either dealt with non-probabilistic datasets (e.g., [1,2,5,7,8]) or gave noninteractive algorithms (e.g., [6,4]). Consequently, the results of earlier work cannot be used to solve the interactive route-search problem that we address in this paper.…”