Processing Of Continuous Range Queries Research Articles

An efficient continuous range query processing scheme in mobile P2P networks

With the development of mobile devices, interest in the mobile P2P-based services has increased. However, the existing schemes spend too much cost to process a continuous range query in mobile P2P network environments. In this paper, we propose a new efficient range query processing scheme in mobile P2P network environments. The proposed scheme consists of two phases. In the query distribution phase, it prunes the peers that are impossible to be included in the query result. In the monitoring phase, the proposed scheme updates the query result incrementally. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme in terms of the number of messages.

SPQI: 이동 환경에서 연속 범위 질의에 대한 효율적인 색인 구조

In this paper, we explore the efficient processing of continuous range queries over a huge number of moving objects, each of which retrieves the moving objects that are currently located within a geographic query region of interest. The moving objects should continually communicate with the server to report their current locations, so as to keep the results of the continuous range queries up-to-date. However, this increases the server workload and involves a enormous amount of communication as the number of continuous range queries and the moving objects becomes enormous. In this paper, we adopt an approach where we leverage available memory and computational resources of the moving objects in order to resolve these problems. To this end, we propose a query indexing structure, referred to as the Space Partitioning Query Index(SPQI), which enables the server to efficiently cooperate with the moving objects for processing continuous range queries. SPQI improves system performance in terms of server workload and communication cost. Through simulations, we show the superiority of SPQI.

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

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.

Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis and additionally considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries, and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. It allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 0.989~84.995 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al.

Continuous Range Query Processing over Moving Objects

In this paper, we propose a continuous range query processing method over moving objects. To efficiently process continuous range queries, we design a main-memory-based query index that uses smaller storage and significantly reduces the query processing time. We show through performance evaluation that the proposed method outperforms the existing methods.

Continuous range monitoring of mobile objects in road networks

In contrast to regular queries that are evaluated only once, a continuous query remains active over a period of time and has to be continuously evaluated to provide up to date answers. We propose a method for continuous range query processing for different types of queries, characterized by mobility of objects and/or queries which all follow paths in an underlying spatial network. The method assumes an available 2D indexing scheme for indexing spatial network data. An appropriately extended R∗-tree, that primarily is used as an indexing scheme for network segments, provides matching of queries and objects according to their locations on the network or their network routes. The method introduces an additional pre-refinement step which generates main-memory data structures to support efficient, incremental reevaluation of continuous range queries in periodically performed refinement steps.

그리드 기반의 질의 색인을 통한 효율적인 연속 영역 질의 처리

In this paper, we propose an efficient continuous range query processing scheme using a modified grid based query indexing to reduce storage spaces and to accelerate processing time. The proposed method has two major features. First, each query has a bit identifier and each cell in a grid has a bit pattern that consists of the bit identifiers of the queries. The bit patterns present the relationship between cells and queries. Using the bit patterns, we can compute quickly what queries overlap a cell in a grid and reduce the number of unnecessary operations by comparing the bit patterns without comparing the query identifiers when we compute the relation between cells and queries. Second, the management of cells in the grid by groups prevents from wasting the storage space through the increase of the length of the bit pattern and increasing the comparison costs of bit patterns. We show through the performance evaluation that the proposed method outperforms the existing methods.

Main Memory Evaluation of Monitoring Queries Over Moving Objects

In this paper we evaluate several in-memory algorithms for efficient and scalable processing of continuous range queries over collections of moving objects. Constant updates to the index are avoided by query indexing. No constraints are imposed on the speed or path of moving objects or fraction of objects that move at any moment in time. We present a detailed analysis of a grid approach which shows the best results for both skewed and uniform data. A sorting based optimization is developed for significantly improving the cache hit-rate. Experimental evaluation establishes that indexing queries using the grid index yields orders of magnitude better performance than other index structures such as R*-trees.