Finding the shortest path in road networks becomes one of important issues in location based services (LBS). The problem of finding the optimal meeting point for a group of users has also been well studied in existing works. In this paper, we investigate a new problem for two users. Each user has his/her own source and destination. However, whether to meet before going to their destinations is with some uncertainty. We model it as minimum path pair (MPP) query, which consists of two pairs of source and destination and a user-specified weight α to balance the two different needs. The result is a pair of paths connecting the two sources and destinations respectively, with minimal overall cost of the two paths and the shortest route between them. To solve MPP queries, we devise algorithms by enumerating node pairs. We adopt a location-based pruning strategy to reduce the number of node pairs for enumeration. An efficient algorithm based on point-to-point shortest path calculation is proposed to further improve query efficiency. We also give two fast approximate algorithms with approximation bounds. Extensive experiments are conducted to show the effectiveness and efficiency of our methods.

Travel planning is one of the important issues in the location-based services (LBS). Traveling salesman problem (TSP) is to find the optimal tour that traverses points exactly once in the minimum total distance. Given the hardness of TSP (NP-hard), TSP query for a given set of points, $$Q$$ , is not widely studied for online LBS, and the nearest-neighbor heuristic is the only heuristic adapted to find TSP-like tours with additional constraints for LBS. The questions to ask are: Is the nearest-neighbor the best in terms of accuracy? Which heuristics among many should we use to process TSP queries online for LBS? In the literature, TSPLIB benchmarks are designed for special cases where the number of points used is large, and the existing synthetic datasets are based on uniform/normal distributions. Both do not reflect the real datasets used in real applications. Therefore, the best heuristics suggested by the TSPLIB and the existing benchmarks need to be reconsidered for LBS setting. In this work, we investigate 22 heuristics and show that the best heuristics in terms of accuracy for LBS are not the ones suggested by the existing work, and identify several heuristics by extensive performance studies over real datasets, TSPLIB benchmarks, the existing synthetic datasets and our new synthetic datasets. Among many issues, we also show that it is possible to get high-quality TSP by precomputing/indexing, even though it is hard to prove by theorem.

We present our system towards live city, called TsingNUS, aiming to provide users with more user-friendly location-aware search experiences. TsingNUS crawls location-based user-generated content from the Web (e.g., Foursquare and Twitter), cleans and integrates them to provide users with rich well-structured data. TsingNUS provides three user-friendly search paradigms: location-aware instant search, location-aware similarity search and direction-aware search. Instant search returns relevant answers instantly as users type in queries letter by letter, which can help users to save typing efforts significantly. Location-aware similarity search enables fuzzy matching between queries and the underlying data, which can tolerate typing errors. The two features boost the search performance and improve the experiences for mobile users who often misspell the keywords due to the limitation of the mobile phone's keyboard. In addition, users have direction-aware search requirements in many applications. For example, a driver on the highway wants to find the nearest gas station or restaurant. She has a search requirement that the answers should be in front of her driving direction. TsingNUS enables direction-aware search to address this problem and allows users to search in specific directions. Moreover, TsingNUS incorporates continuous search to efficiently support continuously moving queries in a client-server system which can reduce the number of queries submitted to the server and communication cost between the client and server. We have implemented and deployed a system which has been commonly used and widely accepted.

Many real-world applications have requirements to support moving spatial keyword queries. For example a tourist looks for top-k "seafood restaurants" while walking in a city. She will continuously issue moving queries. However existing spatial keyword search methods focus on static queries and it calls for new effective techniques to support moving queries efficiently. In this paper we propose an effective method to support moving top-k spatial keyword queries. In addition to finding top-k answers of a moving query, we also calculate a safe region such that if a new query with a location falling in the safe region, we can directly use the answer set to answer the query. To this end, we propose an effective model to represent the safe region and devise efficient search algorithms to compute the safe region. We have implemented our method and experimental results on real datasets show that our method achieves high efficiency and outperforms existing methods significantly.