Keyword-aware Optimal Route Search

TL;DR

This paper introduces the NP-hard keyword-aware optimal route (KOR) query problem, proposing three approximation algorithms that efficiently find routes satisfying user keywords and constraints, with empirical evaluation demonstrating their effectiveness and speed.

Contribution

The paper formulates the KOR problem, proves its NP-hardness, and proposes three approximation algorithms with empirical validation for efficient route planning.

Findings

All algorithms can answer KOR queries efficiently.

BucketBound and Greedy algorithms are faster than OSScaling.

Empirical results demonstrate the accuracy of the algorithms.

Abstract

Identifying a preferable route is an important problem that finds applications in map services. When a user plans a trip within a city, the user may want to find "a most popular route such that it passes by shopping mall, restaurant, and pub, and the travel time to and from his hotel is within 4 hours." However, none of the algorithms in the existing work on route planning can be used to answer such queries. Motivated by this, we define the problem of keyword-aware optimal route query, denoted by KOR, which is to find an optimal route such that it covers a set of user-specified keywords, a specified budget constraint is satisfied, and an objective score of the route is optimal. The problem of answering KOR queries is NP-hard. We devise an approximation algorithm OSScaling with provable approximation bounds. Based on this algorithm, another more efficient approximation algorithm BucketBound is proposed. We also design a greedy approximation algorithm. Results of empirical studies show that all the proposed algorithms are capable of answering KOR queries efficiently, while the BucketBound and Greedy algorithms run faster. The empirical studies also offer insight into the accuracy of the proposed algorithms.