On Trip Planning Queries in Spatial Databases

• Published in: Advances in Spatial and Temporal Databases pp. 273 - 290
• Main Authors: Li, Feifei, Cheng, Dihan, Hadjieleftheriou, Marios, Kollios, George, Teng, Shang-Hua
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Exact sciences and technology; Ground, air and sea transportation, marine construction; Information systems. Data bases; Memory organisation. Data processing; Real Dataset; Road Network; Road transportation and traffic; Software; Synthetic Dataset; Travel Salesman Problem; Metric space; Database query; Information retrieval; Approximation algorithm; Polynomial time; Heuristic method; Best approximation; External memory; Road traffic; Planning; Fast algorithm; Route; Spatial database
• ISBN: 3540281274; 9783540281276
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11535331_16

In this paper we discuss a new type of query in Spatial Databases, called the Trip Planning Query (TPQ). Given a set of points of interest P in space, where each point belongs to a specific category, a starting point S and a destination E, TPQ retrieves the best trip that starts at S, passes through at least one point from each category, and ends at E. For example, a driver traveling from Boston to Providence might want to stop to a gas station, a bank and a post office on his way, and the goal is to provide him with the best possible route (in terms of distance, traffic, road conditions, etc.). The difficulty of this query lies in the existence of multiple choices per category. In this paper, we study fast approximation algorithms for TPQ in a metric space. We provide a number of approximation algorithms with approximation ratios that depend on either the number of categories, the maximum number of points per category or both. Therefore, for different instances of the problem, we can choose the algorithm with the best approximation ratio, since they all run in polynomial time. Furthermore, we use some of the proposed algorithms to derive efficient heuristics for large datasets stored in external memory. Finally, we give an experimental evaluation of the proposed algorithms using both synthetic and real datasets.