Spatial query processing in road networks for wireless data broadcast

Recently, wireless broadcast environments have attracted significant attention due to its high scalability to broadcast information to a large number of mobile subscribers. It is especially a promising and desirable dissemination method for the heavily loaded environment where a great number of the same type of requests are sent from the users. There have been many studies on processing spatial queries via broadcast model recently. However, not much attention is paid to the spatial queries in road networks on wireless broadcast environments. In this paper, we focus on three common types of spatial queries, namely, k nearest neighbor (kNN) queries, range queries and reverse nearest neighbor (RNN) queries in spatial networks for wireless data broadcast. Specially, we propose a novel index for spatial queries in wireless broadcast environments (ISW). With the reasonable organization and the effectively pre-computed bounds, ISW provides a powerful framework for spatial queries. Furthermore, efficient algorithms are designed to cope with kNN, range and RNN queries separately based on ISW. The search space can be obviously reduced and subsequently the client can download as less as possible data for query processing, which can conserve the energy while not significantly influence the efficiency. The detailed theory analysis of cost model and the experimental results are presented for verifying the efficiency and effectiveness of ISW and our methods.     

Moving Query Monitoring in Spatial Network Environments

Moving queries over mobile objects are an important type of query in moving object database systems. In recent years, there have been quite a few works in this area. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. Many techniques have been proposed to address the server bottleneck including one using distributed servers. To address both scalability factors, distributed query processing techniques have been considered. These schemes enable moving objects to participate in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. Most of these techniques, however, assume an open-space environment. Since Euclidean distance is different from network distance, techniques designed specifically for an open space cannot be easily adapted for a spatial network. In this paper, we present a distributed framework which can answer moving query over moving objects in a spatial network. To illustrate the effectiveness of the proposed framework, we study two representative moving queries, namely, moving range queries and moving k-nearest-neighbor queries. Detailed algorithms and communication mechanisms are presented. The simulation studies indicate that the proposed technique can significantly reduce server workload and wireless communication cost.     

Spatial Queries in Wireless Broadcast Systems

Owing to the advent of wireless networking and personal digital devices, information systems in the era of mobile computing are expected to be able to handle a tremendous amount of traffic and service requests from the users. Wireless data broadcast, thanks to its high scalability, is particularly suitable for meeting such a challenge. Indexing techniques have been developed for wireless data broadcast systems in order to conserve the scarce power resources in mobile clients. However, most of the previous studies do not take into account the impact of location information of users. In this paper, we address the issues of supporting spatial queries (including window queries and kNN queries) of location-dependent information via wireless data broadcast. A linear index structure based on the Hilbert curve and corresponding search algorithms are proposed to answer spatial queries on air. Experiments are conducted to evaluate the performance of the proposed indexing technique. Results show that the proposed index and its enhancement outperform existing algorithms significantly.     

Models for motion-based video indexing and retrieval

With the rapid proliferation of multimedia applications that require video data management, it is becoming more desirable to provide proper video data indexing techniques capable of representing the rich semantics in video data. In real-time applications, the need for efficient query processing is another reason for the use of such techniques. We present models that use the object motion information in order to characterize the events to allow subsequent retrieval. Algorithms for different spatiotemporal search cases in terms of spatial and temporal translation and scale invariance have been developed using various signal and image processing techniques. We have developed a prototype video search engine, PICTURESQUE (pictorial information and content transformation unified retrieval engine for spatiotemporal queries) to verify the proposed methods. Development of such technology will enable true multimedia search engines that will enable indexing and searching of the digital video data based on its true content.     

Real-time processing of range-monitoring queries in heterogeneous mobile databases

Unlike conventional range queries, a range-monitoring query is a continuous query. It requires retrieving mobile objects inside a user-defined region and providing continuous updates as the objects move into and out of the region. In this paper, we present an efficient technique for real-time processing of such queries. In our approach, each mobile object is associated with a resident domain, and when an object moves, it monitors its spatial relationship with its resident domain and the monitoring areas inside it. An object reports its location to the server when it crosses over some query boundary or moves out of its resident domain. In the first case, the server updates the affected query results accordingly, while in the second case, the server determines a new resident domain for the object. This distributive approach achieves an accurate and real-time monitoring effect with minimal mobile communication and server processing costs. Our approach also allows a mobile object to negotiate a resident domain based on its computing capability. By having a larger resident domain, a more capable object has less of a chance of moving out of it and having to request a new one. As a result, both communication and server processing costs are reduced. Our comprehensive performance study shows that the proposed technique can be highly scalable in supporting location-based services in a wireless environment that consists of a large number of mobile devices.     

On Searching Continuous k Nearest Neighbors in Wireless Data Broadcast Systems

A continuous nearest neighbor (CNN) search, which retrieves the nearest neighbors corresponding to every point in a given query line segment, is important for location-based services such as vehicular navigation and tourist guides. It is infeasible to answer a CNN search by issuing a traditional nearest neighbor query at every point of the line segment due to the large number of queries generated and the overhead on bandwidth. Algorithms have been proposed recently to support CNN search in the traditional client- server systems but not in the environment of wireless data broadcast, where uplink communication channels from mobile devices to the server are not available. In this paper, we develop a generalized search algorithm for continuous k-nearest neighbors based on Hilbert Curve Index in wireless data broadcast systems. A performance evaluation is conducted to compare the proposed search algorithms with an algorithm based on R-tree Air Index. The result shows that the Hilbert Curve Index-based algorithm is more energy efficient than the R-tree-based algorithm.     

基于对象的空间数据库的方位查询算法

基于方位的空间关系在地理信息系统、图像识别、空间数据库等很多领域中是很重要的，经常被用作空间查询的选择条件。主要研究基于对象的方位查询处理算法，并提出了一种全新的基于模型的开放策略。以OSS为模型的查询算法通过提高传送效率来减少Ⅰ／O和CPU的设备开销，实验数据表明此算法在性能上超越从前的变换查询算法，更适合对复杂的数据集合进行处理。     

Privacy Protected Spatial Query Processing for Advanced Location Based Services

Due to the popularity of mobile devices (e.g., cell phones, PDAs, etc.), location-based services have become more and more prevalent in recent years. However, users have to reveal their location information to access location-based services with existing service infrastructures. It is possible that adversaries could collect the location information, which in turn invades user’s privacy. There are existing solutions for query processing on spatial networks and mobile user privacy protection in Euclidean space. However there is no solution for solving queries on spatial networks with privacy protection. Therefore, we aim to provide network distance spatial query solutions which can preserve user privacy by utilizing K-anonymity mechanisms. In this paper, we propose an effective location cloaking mechanism based on spatial networks and two novel query algorithms, PSNN and PSRQ, for answering nearest neighbor queries and range queries on spatial networks without revealing private information of the query initiator. We demonstrate the appeal of our technique using extensive simulation results.     

Indexing Techniques for Power Management in Multi-Attribute Data Broadcast

In this paper, we discuss the power conservative indexing techniques for managing multi-attribute data broadcast on wireless channels. These indexing techniques, namely, index tree, signature and hybrid, aim at improving the battery power consumption of mobile clients. By taking into account the broadcast management factors such as clustering and scheduling, these three indexing schemes may significantly reduce tune-in time while maintaining a reasonable access time. Cost models for single and multi-attribute query processing are developed. Our performance evaluation shows that the signature and hybrid methods are superior to the index tree method.     

Effective Management of High Rate Spatio-Temporal Queries in Wireless Sensor Networks

Handling high rate queries have always posed a challenge in wireless sensor networks (WSNs) owing to their resource constrained nature. This paper proposes a scheme that performs centralized and distributed optimization to improve the scalability of the high rate spatio-temporal queries in WSNs. Queries are optimized centrally based on multiple criteria such as spatial topological relationships, temporal and attribute correlations. An energy efficient load balanced clustered tree routing based on minimum bounding rectangle spatial indexing scheme is employed to aid the in-network optimization of queries. Two algorithms have been proposed to carry out a centralized and distributed optimization that works adaptively on queries switching between optimal and sub-optimal modes to handle multiple concurrent queries reliably. Simulation results show that the proposed scheme is highly scalable for large scale spatio-temporal queries and also has the added advantage of minimizing the energy consumption due to query and data transmission.     

Energy-efficient skyline query optimization in wireless sensor networks

With the deployment of wireless sensor networks (WSNs) for environmental monitoring and event surveillance, WSNs can be treated as virtual databases to respond to user queries. It thus becomes more urgent that such databases are able to support complicated queries like skyline queries. Skyline query which is one of popular queries for multi-criteria decision making has received much attention in the past several years. In this paper we study skyline query optimization and maintenance in WSNs. Specifically, we first consider skyline query evaluation on a snapshot dataset, by devising two algorithms for finding skyline points progressively without examining the entire dataset. Two key strategies are adopted: One is to partition the dataset into several disjoint subsets and produce the skyline points in each subset progressively. Another is to employ a global filter that consists of some skyline points in the processed subsets to filter out unlikely skyline points from the rest of unexamined subsets. We then consider the query maintenance issue by proposing an algorithm for incremental maintenance of the skyline in a streaming dataset. A novel maintenance mechanism is proposed, which is able to identify which skyline points from past skylines to be the global filter and determine when the global filter is broadcast. We finally conduct extensive experiments by simulations to evaluate the performance of the proposed algorithms on both synthetic and real sensing datasets, and the experimental results demonstrate that the proposed algorithms significantly outperform existing algorithms in terms of network lifetime prolongation.     

Location-Based Spatial Query Processing with Data Sharing in Wireless Broadcast Environments

Location-based spatial queries (LBSQs) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQs is of critical importance with the ever-increasing deployment and use of mobile technologies. We show that LBSQs have certain unique characteristics that traditional spatial query processing in centralized databases does not address. For example, a significant challenge is presented by wireless broadcasting environments, which have excellent scalability but often exhibit high-latency database access. In this paper, we present a novel query processing technique that, while maintaining high scalability and accuracy, manages to reduce the latency considerably in answering location-based spatial queries. Our approach is based on peer-topeer sharing, which enables us to process queries without delay at a mobile host by using query results cached in its neighboring mobile peers. We demonstrate the feasibility of our approach through a probabilistic analysis, and we illustrate the appeal of our technique through extensive simulation results.     

An Efficient PAB‐Based Query Indexing for Processing Continuous Queries on Moving Objects

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)‐based query index. We project a two‐dimensional continuous range query on each axis to get two one‐dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment‐encoded squares‐based indexing method, which is one of the most recently proposed methods.     

一种高效的OLAP最值查询算法

In the process of analyzing the large-scale network security situation, the data we faced are always flooded and messy, and the information is difficult to obtain with respond to the query timely. Online analytical processing which use the Data-cube as a data source directly, calculated all or part of the Data-cube in advance, and it can reduce the query response time significantly. This paper considers a class of queries,called the Partial-MAX/MIN query. We introduce Rank Decision Tree(RD-Tree) and it’s searching algorithm for efficient processing of the partial-max/min queries.Through experiments, we show our approach has an efficient processing capability for partial-max/min queries.     

SQLS: A Storm-Based Query Language System for Real-Time Stream Data Analysis

In this work,a Storm-based query language System (SQLS) is proposed for real-time data stream analysis.The system is compatible with Continuous query language (CQL) specification.It supports both continuous queries and one-time queries over streaming data,and meets the requirements of user experience (traditional SQL queries) and QoS (such as real-time and throughput).In order to better meet the requirement of throughput and enhance the processing efficiency,the load shedding algorithm and cache optimization are employed during the implementation of SQL-like operators.Finally,performance testing of the proposed SQLS has been conducted on standalone Storm platform and Storm clusters.Experimental resuits show that our system can not only meet the needs of users,but also extend the function of real-time streaming queries processing.     

A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems

When the mobile environment consists of light-weight devices, the energy consumption of location-based services (LBSs) and the limited bandwidth of the wireless network become important issues. Motivated by this, we propose new spatial query processing algorithms to support Mobile Continuous Nearest Neighbor Query (MCNNQ) in wireless broadcast environments. Our solution provides a general client–server architecture for answering MCNNQ on objects with unknown, and possibly variable, movement types. Our solution enables the application of spatio-temporal access methods specifically designed for a particular type, to arbitrary movements without any false misses. Our algorithm does not require any conventional spatial index for MCNNQ processing. It can be adapted to static or moving objects, and does not require additional knowledge (e.g., direction of moving objects) beyond the maximum speed and the location of each object. Extensive experiments demonstrate that our location-based data dissemination algorithm significantly outperforms index-based solutions.     

Non-Line-of-Sight Localization in Multipath Environments

Location-based spatial queries (LBSQs) refer to spatial queries whose answers rely on the location of the inquirer. Efficient processing of LBSQs is of critical importance with the ever-increasing deployment and use of mobile technologies. We show that LBSQs have certain unique characteristics that the traditional spatial query processing in centralized databases does not address. For example, a significant challenge is presented by wireless broadcasting environments, which have excellent scalability but often exhibit high-latency database access. In this paper, we present a novel query processing technique that, though maintaining high scalability and accuracy, manages to reduce the latency considerably in answering LBSQs. Our approach is based on peer-to-peer sharing, which enables us to process queries without delay at a mobile host by using query results cached in its neighboring mobile peers. We demonstrate the feasibility of our approach through a probabilistic analysis, and we illustrate the appeal of our technique through extensive simulation results.     

Spatial Indexing for Video Databases

Spatial reasoning techniques based on 2D string systems have been successfully applied to indexing pictorial databases. We demonstrate how similar techniques may be extended to represent video sequences, and how this representation may be compressed. Query schemes for both single frame queries, and full subsequence queries are presented. The strength of these query schemes is that the query process does not require the expansion of the compressed notation during query evaluation.     

移动P2P网络中能量有效连续查询研究

在移动P2P网络中一个基本应用问题是访问分布式空间数据库中的数据对象,同时节省电量.文中提出了在移动P2P网络中能量有效连续查询处理（Energy Efficient Continuous Query Processing,EECQP）算法,包括范围查询和k个最近邻居节点查询（k-nearest-neighbor queries, k-NN）.EECQP算法能够提供移动用户找到一个有质量保证的连续查询结果.EECQP算法主要的想法是允许用户与节点合作,持续保存查询结果,而不是总是从头开始处理查询,从而减少通信开销.实验结果表明所提出的EECQP算法对于范围查询和k个最近邻居节点查询在通信开销和QoS保证是能量有效和可扩展的.     

An intelligent user interface for browsing satellite data catalogs

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given.