Chunan Chen

An Air Index for Spatial Query Processing in Road Networks

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 build the index to carry some pre-computation information of each region. We also propose multiple client-side 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.

Quick map matching using multi-core CPUs

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.

Energy-efficient shortest path query processing on air

Wireless broadcast provides a scalable and secure spatial data dissemination approach for geographical applications in wireless mobile environments. Among various location-based services, the shortest path query on road networks is one of the most popular and essential services in our daily life. In this paper, we propose an energy-efficient scheme for on air shortest path query processing on road networks, which leverages an elaborate air index called BagIndex based upon the novel Hilbert-based heuristic tree decomposition for the road networks. Experimental results show that the proposed approach incurs less energy consumption on both communication and computation than the previous schemes.

Merged aggregate nearest neighbor query processing in road networks

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.

An incremental approach to closest pair queries in spatial networks using best-first search

This paper addresses the problem of k Closest Pairs (kCP) query in spatial network databases. A Best-First search approach namely BFCP (Best-First Closest Pair) is proposed. Given two data sets of objects in a spatial network, BFCP first finds the 1st CP by computing the 1st NN (nearest neighbor) of each object in the set with smaller cardinality. Then BFCP retrieves the 2nd , 3rd , ... , kth CP in an incremental way by searching the next NN of the currently found CPs source point. Furthermore, a novel buffer replacement policy called MDU (Minimum Distance Unit) is proposed to reduce I/O cost of BFCP. Unlike LRU, which records only the last reference time, the MDU policy considers both temporal locality and spatial locality when selecting a buffer page as the victim. A comprehensive experimental study is conducted to demonstrate the advantage of BFCP and MDU.

On Efficient Aggregate Nearest Neighbor Query Processing in Road Networks

An aggregate nearest neighbor (ANN) query returns a point of interest (POI) that minimizes an aggregate function for multiple query points. In this paper, we propose an efficient approach to tackle ANN queries in road networks. Our approach consists of two phases: searching phase and pruning phase. In particular, we first continuously compute the nearest neighbors (NNs) for each query point in some specific order to obtain the candidate POIs until all query points find a common POI. Second, we filter out the unqualified POIs based on the pruning strategy for a given aggregate function. The two-phase process is repeated until there remains only one candidate POI, and the remained one is returned as the final result. In addition, we discuss the partition strategies for query points and the approximate ANN query for the case where the number of query points is huge. Extensive experiments using real datasets demonstrate that our proposed approach outperforms its competitors significantly in most cases.

Aggregate keyword routing in spatial database

Due to the proliferation of Location-Based Service and popularity of online geo-tagged web pages, spatial keyword search has attracted significant attention from both academic and industrial communities. In this paper, we study the problem of finding the nearest aggregate point from multiple query points travelling through a set objects described by a given set of keywords, as well as the optimal routes from the query points to the aggregate point. This problem is defined as the Aggregate Keyword Routing (AKR) Query. We devise an exact algorithm to AKR query based on ellipse pruning. Next we propose an efficient approximate algorithm for AKR: Center Based Assignment (CBA). The performance of the proposed algorithms are evaluated with real data, the results demonstrate the efficiency and the effectiveness.

Fast optimal aggregate point search for a merged set on road networks

Aggregate nearest neighbor query, which returns an optimal target 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 two algorithms to process MANN query on road networks when aggregate function is max. Then, we extend the algorithms to support other aggregate functions (e.g., sum). Extensive experiments are conducted to examine the behaviors of the solutions in terms of five parameters affecting the performance. The overall experiments show that our strategies to minimize the response time are effective.