Optimized skyline queries on road networks using nearest neighbors

Skyline queries are used with data extensive applications, such as mobile location-based services, to support multi-criteria decision-making and to prune the data space by returning the most “interesting” data points. Most interesting data points are the points, which are not dominated by any other point. Spatial network skyline query is a subset of the skyline query problem where data points are nodes in a road network and the attributes of the data points are network distance relative to a set of query points. Spatial network skyline query’s problem is the need to calculate the attributes with an expensive distance calculation operation. Previous works (Deng et al. Proceedings of the 23th international conference on data engineering, 796–805, 2007), Sharifzadeh et al. Proceedings of the 32nd international conference on very large databases, 751–762, 2009) that addressed this problem involved extensive network distance calculation between the query points and data points. A new algorithm that requires a remarkably less number of network distance calculations is proposed in this work. Our approach uses a progressive nearest neighbor algorithm to minimize the set of candidates then evaluates those candidates by only comparing them to a subset of discovered skyline points. Experiments showed the effectiveness of our algorithm compared to previous works.     

A connectivity index for moving objects in an indoor cellular space

With the currently available indoor positioning devices such as RFID, Bluetooth and WI-FI, the locations of moving objects constitute an important foundation for a variety of applications such as the tracking of moving objects, security and way finding. Many studies have proven that most individuals spend their lives in indoor environments. Therefore, in this paper, we propose a new index structure for moving objects in cellular space. The index is based on the connectivity (adjacency) between the indoor environment cells and can effectively respond to the spatial indoor queries and enable efficient updates of the location of a moving object in indoor space. An empirical performance study suggests that the proposed indoor-tree in terms of measurements and performance is effective, efficient and robust.     

Mobile broadcast services with MIMO antennae in 4G wireless networks

The deployment of wireless data broadcast to empower mobile information services as a resource-conserving means offers significant benefits due to the scalability feature offered by the technology. In this paper, we present a novel and holistic data broadcast management approach in 4 G wireless networks with multiple-input multiple-output (MIMO) antennae. The proposed scheme consists of three elements, namely: (i) broadcast ordering; (ii) Global indexing; and (iii) merging data structure. With the integration of these elements, we expect to obtain substantial efficiency for mobile computing clients when retrieving data on-air. We have experimentally evaluated the performance of the proposed model including comparison with the relevant schemes. The results from the experiments affirm the effectiveness of our proposed approach in respect to minimizing query access time and conserving energy utilization of the clients.     

Indexing moving objects for directions and velocities queries

Moving object databases are required to support different types of queries with a large number of moving objects. New types of queries namely directions and velocity queries (DV queries), are to be supported and covered. The TPR-tree and its successors are efficient indexes that support spatio-temporal queries for moving objects. However, neither of them support the new DV queries. In this paper, we propose a new index for moving objects based on the TPR*-tree, named Direction and Velocity of TPR*-tree or DV-TPR*-tree, in order to build data a structure based on the spatial, direction and velocity domains. DV-TPR*-tree obtains an ideal distribution that supports and fulfils the new query types (DV queries). Extensive performance studies show that the query performance of DV-TPR*-tree outperforms the TPR-tree and its successors.     

Voronoi-based multi-level range search in mobile navigation

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.