Abstract. Location-Based Services (LBSs) are becoming more social and Social Networks (SNs) are increasingly including location components. Geo-Social Networks are bridging the gap between virtual and physical social networks. In this paper, we propose a new type of query called Circle of Friend Query (CoFQ) to allow finding a group of friends in a Geo-Social network whose members are close to each other both socially and geographically. More specifically, the members in the group have tight social relationships with each other and they are constrained in a small region in the geospatial space as measured by a "diameter" that integrates the two aspects. We prove that algorithms for finding the Circle of Friends (CoF ) of size k is NP-hard and then propose an ε-approximate solution. The proposed ε-approximate algorithm is guaranteed to produce a group of friends with diameter within ε of the optimal solution. The performance of our algorithm is tested on the real dataset from Foursquare. The experimental results show that our algorithm is efficient and scalable: the ε-approximate algorithm runs in polynomial time and retrieves around 95% of the optimal answers for small ε.

Spatial queries such as range query and kNN query in road networks have received a growing number of attention in real life. Considering the large population of the users and the high overhead of network distance computation, it is extremely important to guarantee the efficiency and scalability of query processing. Motivated by the scalable and secure properties of wireless broadcast model, this paper presents an air index called Network Partition Index (NPI) to support efficient spatial query processing in road networks via wireless broadcast. The main idea is to partition the road network into a number of regions and then to build an index to carry some pre-computation information of each region. We also propose multiple clientside algorithms to facilitate the processing of different spatial queries such as kNN query, range query and CNN query. A comprehensive experimental study has been conducted to demonstrate the efficiency of our scheme.Index Terms-wireless data broadcast, kNN query, air indexing, road network.

The ACM SIGSPATIAL Cup 2012 is about map matching, a problem of correctly matching a sequence of GPS sampling points to the roads on a digital map. This paper describes one of the winning submissions of the competition. The approach applies multi-threading technology to map matching in order to reduce running time and we propose an improvement to the Hidden Markov Model (HMM) map matching algorithm.

Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted to examine the behaviors of the solutions and the overall experiments show that our strategies to minimize the response time are effective and achieve several orders of magnitude speedup compared with the baseline methods.

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