Kefeng Xuan

Voronoi-Based Continuous

Digital ecosystems are formed by “digital organisms” in complex, dynamic, and interrelated ecosystems and utilize multiple technologies to provide cost-efficient digital services and value-creating activities. A distributed wireless mobile network that serves as the underlying infrastructure to digital ecosystems provides important applications to the digital ecosystems, two of which are mobile navigation and continuous mobile information services. Most information and query services in a mobile environment are continuous mobile query processing or continuous k nearest neighbor (CKNN), which finds the locations where interest points or interest objects change while mobile users are moving. These locations are known as “split nodes.” All of the existing works on CKNN divide the query path into segments, which is a segment of road separated by two intersections, and then, the process to find split nodes is applied to each segment. Since there are many segments (due to many intersections, obviously), processing each segment is naturally inefficient. In this paper, we propose an alternative solution to overcome this problem. We use the Voronoi diagram for CKNN [called Voronoi CKNN (VCKNN)]. Our proposed approach does not need to divide the query path into segments, hence improving the overall query processing performance. Our experiment verified the applicability of the VCKNN approach to solve CKNN queries.

k

With the wide availability of mobile devices (smart phones, iPhones, etc.), mobile location-based queries are increasingly in demand. One of the most frequent queries is range search which returns objects of interest within a pre-defined area. Most of the existing methods are based on the road network expansion method - expanding all nodes (intersections and objects) and computing the distance of each node to the query point. Since road networks are extremely complex, node expansion approaches are inefficient. In this paper, we propose a method, Voronoi Range Search (VRS) based on the Voronoi diagram, to process range search queries efficiently and accurately by partitioning the road networks to some special polygons. Then we further propose Voronoi Continuous Range (VCR) to satisfy the requirement for continuous range search queries (moving queries) based on VRS. Our empirical experiments show that VRS and VCR surpass all their rivals for both static and moving queries.

Nearest Neighbor Search in Mobile Navigation

Range search query processing has become one of the most important technologies in spatial and mobile databases. Most literature focuses on static range search extended from one point on both Euclidean distance and actual network distance, but there are only a few methods which can properly solve the problem for moving users, such as searching objects of interest on a road within a certain range. Although there are some techniques, which address continuous search, most approaches are absorbed in the KNN (k nearest neighbour) queries which are a different research area. In this paper, we propose two new methods to process continuous range search query in mobile computing. One is constructed using R-tree index based on Euclidean distance, and the other addresses the requirement on actual network distance.

Voronoi-based range and continuous range query processing in mobile databases

Due to the universality and importance of range search queries processing in mobile and spatial databases as well as in geographic information system (GIS), numerous approaches on range search algorithms have been proposed in recent years. But ordinary range search queries focus only on a specific type of point objects. For queries which require to retrieve objects of interest locating in a particular region, ordinary range search could not get the expected results. In addition, most existing range search methods need to perform a searching on each road segments within the pre-defined range, which decreases the performance of range search. In this paper, we design a weighted network Voronoi diagram and propose a high-performance multilevel range search query processing that retrieves a set of objects locating in some specified region within the searching range. The experimental results show that our proposed algorithm runs very efficiently and outperforms its main competitor.

Continuous Range Search Query Processing in Mobile Navigation

Most query search on road networks is either to find objects within a certain range (range search) or to find K nearest neighbors (KNN) on the actual road network map. In this paper, we propose a novel query, that is, incremental k nearest neighbor (iKNN). iKNN can be defined as given a set of candidate interest objects, a query point and the number of objects k, find a path which starts at the query point, goes through k interest objects and the distance of this path is the shortest among all possible paths. This is a new type of query, which can be used when we want to visit k interest objects one by one from the query point. This approach is based on expanding the network from the query point, keeping the results in a query set and updating the query set when reaching network intersection or interest objects. The basic theory of this approach is Dijkstras algorithm and the Incremental Network Expansion (INE) algorithm. Our experiment verified the applicability of the proposed approach to solve the queries, which involve finding incremental k nearest neighbor.

Voronoi-based multi-level range search in mobile navigation

Digital ecosystems which have been inspired by natural systems aim to address the complexity of digital world which is expected to have the capabilities to self-organize, is scalable, and is attainable. Spatial networks consisting of geospatial objects and paths that link the objects form a digital ecosystem in the context of geoinformatics. With the recent development of mobile devices using inexpensive wireless networks, applications to access interest objects and their paths in the spatial world are getting more in demand. In this paper, we introduce the concept of path-based k nearest neighbor (pkNN). Given a set of candidate interest objects, a query point, and the number of objects k, pkNN finds the shortest path that goes through all k interest objects with the minimum shortest distance among all possible paths. pkNN is useful when users would like to visit all k interest objects one by one from the query point, in which pkNN will give the users the shortest path. We have addressed the complexities of the pkNN method, covering various looping paths, U-turn, and the possibilities to encounter local minima. Our performance evaluations show that pkNN performs well in respect to various object densities on the map due to our proposed pruning methods to reduce the search space.

INCREMENTAL K-NEAREST-NEIGHBOR SEARCH ON ROAD NETWORKS

Range search is one of the most common queries in the spatial databases and geographic information systems (GIS). Most range search processing depends on the length of the distance that expresses the relative position of the objects of interest in the Euclidean space or road networks. But, in reality, the expected result is normally constrained by other factors (e.g. number of spatial objects, pre‐defined area, and so forth.) rather than the distance alone; hence, range search should be comprehensively discussed in various scenarios. In this paper, we propose two constrained range search approaches based on network Voronoi diagram, namely Region Constrained Range (RCR) and k nearest neighbor Constrained Range (kCR), which make the range search query processing more flexible to satisfy different requirements in a complex environment. The performance of these approaches is analyzed and evaluated to illustrate that both of them can process constrained range search queries very efficiently. Copyright

Path

One of the most frequent queries in spatial and mobile databases is range search, which is originated from the construction of R-tree that limits the spatial database application to Euclidean distance. Nowadays, Geographic Information System (GIS) demands the applications to be practicable for factual distance, normally identified as network distance. Even though some algorithms are engaged in this area, network distance range search is still a time consuming and storage space occupation task. In this paper, we propose a novel approach which is based on Network Voronoi Diagram that is diffusely used in geometrical analysis. We are looking into how to improve the performance of range search query processing using Network Voronoi Diagram.

k\hbox{NN}

Existing work on k nearest neighbor (k NN) in spatial/mobile query processing focuses on single object types. Furthermore, they do not consider optimum path in KNN. In this paper, we focus on multiple type k NN whereby the interest points are of multiple types. Additionally, we also consider an optimum path to reach the interest points. We propose three different query types involving multiple object types. Our algorithms adopt the network Voronoi Diagram (NVD). We describe two ways to solve multiple types of KNN queries: one is to create NVD for each object type, and two is to create one NVD for all objects. The comparison between these two approaches is presented in performance evaluation section.

Query Processing in Mobile Systems

Due to the boom of GIS and GPS, various queries in spatial and mobile database become hot spot issues nowadays, typically, range search and k nearest neighbour queries. Both problems have undergone extensive development for many years, from focusing on the Euclidean distance to network distance, and from dealing with static objects to moving objects. In every step forward, there have been many literature reports, but only a few have dealt with Continuous Range Search (CRS) query processing. Even though the existing approach to CRS has an acceptable performance, it is still impractical because the query path needs to be segmented. In this paper, we propose a new approach based on network Voronoi diagram for range search, where the query path does not need to be segmented.