A distributed spatial index for error-prone wireless data broadcast

Information is valuable to users when it is available not only at the right time but also at the right place. To support efficient location-based data access in wireless data broadcast systems, a distributed spatial index (called DSI) is presented in this paper. DSI is highly efficient because it has a linear yet fully distributed structure that naturally shares links in different search paths. DSI is very resilient to the error-prone wireless communication environment because interrupted search operations based on DSI can be resumed easily. It supports search algorithms for classical location-based queries such as window queries and kNN queries in both of the snapshot and continuous query modes. In-depth analysis and simulation-based evaluation have been conducted. The results show that DSI significantly out-performs a variant of R-trees tailored for wireless data broadcast environments.     

A Hierarchical Grid Index (HGI), spatial queries in wireless data broadcasting

The main requirements for spatial query processing via mobile terminals include rapid and accurate searching and low energy consumption. Most location-based services (LBSs) are provided using an on-demand method, which is suitable for light-loaded systems where contention for wireless channels and server processing is not severe. However, as the number of users of LBSs increases, performance deteriorates rapidly since the servers’ capability to process queries is limited. Furthermore, the response time of a query may significantly increase with the concentration of users’ queries in a server at the same time. That is because the server has to check the locations of users and potential objects for the final result and then individually send answers to clients via a point-to-point channel. At this time, an inefficient structure of spatial index and searching algorithm may incur an extremely large access latency. To address this problem, we propose the Hierarchical Grid Index (HGI), which provides a light-weight sequential location-based index structure for efficient LBSs. We minimize the index size through the use of hierarchical location-based identifications. And we support efficient query processing in broadcasting environments through sequential data transfer and search based on the object locations. We also propose Top-Down Search and Reduction-Counter Search algorithms for efficient searching and query processing. HGI has a simple structure through elimination of replication pointers and is therefore suitable for broadcasting environments with one-dimensional characteristics, thus enabling rapid and accurate spatial search by reducing redundant data. Our performance evaluation shows that our proposed index and algorithms are accurate and fast and support efficient spatial query processing.     

Mobile service oriented architectures for NN-queries

The ever-increasing number of mobile device users has also raised the users’ expectation of mobile services accordingly. This phenomenon has given pressures to the mobile service providers to improve their services in order to stay competitive in the market. The service oriented approach is seen to be a promising scheme for mobile services. This paper presents mobile service oriented architectures for Nearest-Neighbor (NN) queries that are classified into five categories, namely (i) intermittent query mobile services, (ii) continuous query mobile services, (iii) context-aware mobile services, (iv) continuous moving object query mobile services, and (v) data broadcast mobile services. These services incorporate query, location and context-aware services, ontological context model, and broadcast. The proposed architectures are concerned with mobile services for clients on the move requesting services based on their current location, which is arguably the most important feature in a wireless environment. Furthermore, we also discuss the Quality-of-Service (QoS) requirement for mobile services in which request latency time is one of the most important parameters to consider. Some analytical models for query latency measurement are presented and the results are compared with the simulation experiments.     

Disseminating dependent data in wireless broadcast environments

In wireless mobile environments, data broadcasting is an effective approach to disseminate information to mobile clients. In some applications, the access pattern of all the data can be represented by a weighted DAG. In this paper, we explore how to efficiently generate the broadcast schedule in a wireless environment for the data set having a weighted DAG access pattern. Such a broadcast schedule not only minimizes the access latency but also is a topological ordering of the DAG. Minimized access latency ensures the quality of service (QoS). We prove that it is NP-hard to find an optimal broadcast schedule and provide some heuristics. After giving an analysis for these heuristics on the latency and complexity, we implement all the proposed heuristics to compare their performance. Recommended by: Sunil Prabhakar     

Broadcast-Based Spatial Queries

Indexing techniques have been developed for wireless data broadcast environments, in order to conserve the scarce power resources of the mobile clients. However, the use of interleaved index segments in a broadcast cycle increases the average access latency for the clients. In this paper, the broadcast-based spatial query processing methods (BBS) are presented for the location-based services. In the BBS, broadcasted data objects are sorted sequentially based on their locations, and the server broadcasts the location dependent data along with an index segment. Then, a sequential prefetching and caching scheme is designed to reduce the query response time. The performance of this scheme is investigated in relation to various environmental variables, such as the distributions of the data objects, the average speed of the clients and the size of the service area.     

Power-aware data retrieval protocols for indexed broadcast parallel channels

In pervasive and mobile computing environments, “timely and reliable” access to public data requires methods that allow quick, efficient, and low-power access to information to overcome technological limitations of wireless communication and access devices. The literature suggests broadcasting (one-way communication) as an effective way to disseminate the public data to mobile devices. Within the scope of broadcasting, the response time and energy consumption of retrieval methods have been used as the performance metrics for measuring the effectiveness of different access methods. The hardware and architecture of the mobile units offer different operational modes that consume different energy levels. Along with these architectural and hardware enhancements, techniques such as indexing, broadcasting along parallel channels, and efficient allocation and retrieval protocols can be used to minimize power consumption and access latency.In general, the retrieval methods attempt to determine the optimal access pattern for retrieving the requested data objects on parallel broadcast channels. The employment of heuristics provides a methodology for such ideal path planning solutions. Using informative heuristics and intelligent searches of an access forest can provide a prioritized cost evaluation of access patterns for requested data objects and, hence, an optimal path for the access of requested data on broadcast air channels.This paper examines two scheduling methods that along with a set of heuristics generate and facilitate the access patterns for retrieving data objects in the presence of conflicts in an indexed parallel broadcast channel environment. A simulation of the proposed schemes is presented for analyzing the relationship between response time and power consumption.     

k-Nearest neighbor query processing algorithm for cloaking regions towards user privacy protection in location-based services

Due to the advancement of wireless internet and mobile positioning technology, the application of location-based services (LBSs) has become popular for mobile users. Since users have to send their exact locations to obtain the service, it may lead to several privacy threats. To solve this problem, a cloaking method has been proposed to blur users’ exact locations into a cloaked spatial region with a required privacy threshold (k). With the cloaked region, an LBS server can carry out a k-nearest neighbor (k-NN) search algorithm. Some recent studies have proposed methods to search k-nearest POIs while protecting a user’s privacy. However, they have at least one major problem, such as inefficiency on query processing or low precision of retrieved result. To resolve these problems, in this paper, we propose a novel k-NN query processing algorithm for a cloaking region to satisfy both requirements of fast query processing time and high precision of the retrieved result. To achieve fast query processing time, we propose a new pruning technique based on a 2D-coodinate scheme. In addition, we make use of a Voronoi diagram for retrieving the nearest POIs efficiently. To satisfy the requirement of high precision of the retrieved result, we guarantee that our k-NN query processing algorithm always contains the exact set of k nearest neighbors. Our performance analysis shows that our algorithm achieves better performance in terms of query processing time and the number of candidate POIs compared with other algorithms.     

移动数据库广播的并发控制算法研究

在基于数据广播的移动信息服务中,由于更新数据和广播数据两个过程是并发执行的,所以移动客户将面临接收数据不一致性问题。为此,该文提出了一种基于规则的并发控制算法来解决以上问题。在允许同步更新的情况下采用事件-条件-操作规则来进行无冲突的数据广播。该算法对移动机和数据库服务器的影响很小,同时又为基于广播的移动计算提供了数据一致性服务。     

Countering overlapping rectangle privacy attack for moving kNN queries

An important class of LBSs is supported by the moving k nearest neighbor (MkNN) query, which continuously returns the k nearest data objects for a moving user. For example, a tourist may want to observe the five nearest restaurants continuously while exploring a city so that she can drop in to one of them anytime. Using this kind of services requires the user to disclose her location continuously and therefore may cause privacy leaks derived from the user's locations. A common approach to protecting a user's location privacy is the use of imprecise locations (e.g., regions) instead of exact positions when requesting LBSs. However, simply updating a user's imprecise location to a location-based service provider (LSP) cannot ensure a user's privacy for an MkNN query: continuous disclosure of regions enable LSPs to refine more precise location of the user. We formulate this type of attack to a user's location privacy that arises from overlapping consecutive regions, and provide the first solution to counter this attack. Specifically, we develop algorithms which can process an MkNN query while protecting the user's privacy from the above attack. Extensive experiments validate the effectiveness of our privacy protection technique and the efficiency of our algorithm.     

An efficient nonuniform index in the wireless broadcast environments

Data broadcast is an efficient dissemination method to deliver information to mobile clients through the wireless channel. It allows a huge number of the mobile clients simultaneously access data in the wireless environments. In real-life applications, more popular data may be frequently accessed by clients than less popular ones. Under such scenarios, Acharya et al.’s Broadcast Disks algorithm (BD) allocates more popular data appeared more times in a broadcast period than less popular ones, i.e., the nonuniform broadcast, and provides a good performance on reducing client waiting time. However, mobile devices should constantly tune in to the wireless broadcast channel to examine data, consuming a lot of energy. Using index technologies on the broadcast file can reduce a lot of energy consumption of the mobile devices without significantly increasing client waiting time. In this paper, we propose an efficient nonuniform index called the skewed index, SI, over BD. The proposed algorithm builds an index tree according to skewed access patterns of clients, and allocates index nodes for the popular data more times than those for the less popular ones in a broadcast cycle. From our experimental study, we have shown that our proposed algorithm outperforms the flexible index and the flexible distributed index.     

An energy efficient re-access scheme for data caching in data broadcast of a mobile computing environment

Data caching is used to improve the response time and the power consumption of a mobile client in a mobile computing environment. To enhance the performance of data caching, one needs to improve the hit ratio and to reduce the cost in processing a cache miss. In a mobile computing environment, a cached data item of a mobile client needs to remain up-to-date with respect to its corresponding data item in the server. A cached data item which is out of date is called a cached invalidated data item. Accessing a cached invalidated data item can be regarded as processing a cache miss. To access a cached invalidated data item, a mobile client needs to download the new content of the data item from the broadcast channel. This operation is called a re-access operation in this paper. Re-accessing a cached invalidated data item incurs large tuning time overhead. In this paper, we propose a re-access scheme that reduces this overhead by allowing a mobile client to access a cached invalidated data item from the broadcast channel without accessing indices. We analyze the performance of the proposed scheme and validate the analysis through experiments. The experiments showed that the proposed scheme significantly reduces the tuning time of a mobile client. Furthermore, the proposed scheme is robust in the sense that it allows changes on the broadcast structure in data broadcasting.     

Preference-oriented mining techniques for location-based store search

With the development of wireless telecommunication technologies, a number of studies have been done on the issues of location-based services due to wide applications. Among them, one of the active topics is the location-based search. Most of previous studies focused on the search of nearby stores, such as restaurants, hotels, or shopping malls, based on the user’s location. However, such search results may not satisfy the users well for their preferences. In this paper, we propose a novel data mining-based approach, named preference-oriented location-based search (POLS), to efficiently search for k nearby stores that are most preferred by the user based on the user’s location, preference, and query time. In POLS, we propose two preference learning algorithms to automatically learn user’s preference. In addition, we propose a ranking algorithm to rank the nearby stores based on user’s location, preference, and query time. To the best of our knowledge, this is the first work on taking temporal location-based search with automatic user preference learning into account simultaneously. Through experimental evaluations on the real dataset, the proposed approach is shown to deliver excellent performance.     

移动实时事务预留广播空间的验证协议

在无线数据广播环境中,从移动客户机到服务器的上行带宽非常有限,传统的并发控制协议不适合这种不对称通信环境.本文提出一种可变乐观并发控制协议,服务器周期性向移动客户机广播数据对象,同时把一个广播周期分成若干个子周期,在两个连续的子周期间,一个保留空间被预留来存放服务器更新事务在第一个子周期开始后修改的所有数据对象,移动只读事务通过比较服务器更新事务提交的写集合和自己读集合来自主进行一致性验证,如果只读事务不能通过部分验证,它不会被草率地夭折并重新启动,相反,应用一个改进的向前验证策略,帮助移动只读事务争取更多提交机会.最后,本文进行充分的实验对提出的算法性能进行了评价.     

数据广播环境下移动实时事务的有效并发控制

在无线数据广播环境下,从移动客户机到数据库服务器的上行带宽非常有限.传统的并发控制协议不适合这种不对称通讯环境.本文结合乐观并发控制协议和时标排序协议提出一种并发控制协议.只读事务满足弱一致性,包含所有更新事务和一个移动只读事务的可串行化图是非循环的.使用本文提出的并发控制协议,移动只读事务能够自主执行,移动更新事务能够较早地检测到数据冲突.模拟试验显示,我们提出的协议相对其它已经存在的并发控制协议来说,能够更好地满足事务截止期.     

Efficiently processing snapshot and continuous reverse k nearest neighbors queries

Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone that is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location-based services, marketing and decision support systems. It can also be used to efficiently process RkNN queries. First, we present efficient algorithm to compute the influence zone. Then, based on the influence zone, we present efficient algorithms to process RkNN queries that significantly outperform existing best-known techniques for both the snapshot and continuous RkNN queries. We also present a detailed theoretical analysis to analyze the area of the influence zone and IO costs of our RkNN processing algorithms. Our experiments demonstrate the accuracy of our theoretical analysis. This paper is an extended version of our previous work (Cheema et?al. in Proceedings of ICDE, pp. 577–588, 2011). We make the following new contributions in this extended version: (1) we conduct a rigorous complexity analysis and show that the complexity of one of our proposed algorithms in Cheema et?al. (Proceedings of ICDE, pp. 577–588, 2011) can be reduced from O(m ²) to O( km) where m?>?k is the number of objects used to compute the influence zone, (2) we show that our techniques can be applied to dimensionality higher than two, and (3) we present efficient techniques to handle data updates.     

Exploiting data mining techniques for broadcasting data in mobile computing environments

Mobile computers can be equipped with wireless communication devices that enable users to access data services from any location. In wireless communication, the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. We propose two methods aiming to reduce client access latency of broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. With the first method, the data items in the broadcast disk are organized in such a way that the items requested subsequently are placed close to each other. The second method focuses on improving the cache hit ratio to be able to decrease the access latency. It enables clients to prefetch the data from the broadcast disk based on the rules extracted from previous data request patterns. The proposed methods are implemented on a Web log to estimate their effectiveness. It is shown through performance experiments that the proposed rule-based methods are effective in improving the system performance in terms of the average latency as well as the cache hit ratio of mobile clients.     

An indexing scheme for energy-efficient processing of content-based retrieval queries on a wireless data stream

Wireless data broadcasting is a popular data delivery approach in mobile computing environments, where the broadcasting servers usually adopt indexing schemes for mobile clients to energy-efficiently access data on a wireless broadcast stream. However, conventional indexing schemes use primary key attribute values to construct tree structures. Therefore, these schemes do not support content-based retrieval queries such as partial-match queries and range-queries. This paper proposes an indexing method that supports content-based retrieval queries on a wireless data stream. The method uses a tree-structured index, called B2V-Tree, which is composed of bit-vectors that are generated from data records through multi-attribute hashing. Through analysis and experiments, the effectiveness of the proposed method is shown.     

SMashQ: spatial mashup framework for k-NN queries in time-dependent road networks

The k-nearest-neighbor (k-NN) query is one of the most popular spatial query types for location-based services (LBS). In this paper, we focus on k-NN queries in time-dependent road networks, where the travel time between two locations may vary significantly at different time of the day. In practice, it is costly for a LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the best route from a user to a spatial object of interest in terms of the travel time. Thus, we design SMashQ, a server-side spatial mashup framework that enables a database server to efficiently evaluate k-NN queries using the route information and travel time accessed from an external Web mapping service, e.g., Microsoft Bing Maps. Due to the expensive cost and limitations of retrieving such external information, we propose three shared execution optimizations for SMashQ, namely, object grouping, direction sharing, and user grouping, to reduce the number of external Web mapping requests and provide highly accurate query answers. We evaluate SMashQ using Microsoft Bing Maps, a real road network, real data sets, and a synthetic data set. Experimental results show that SMashQ is efficient and capable of producing highly accurate query answers.     

Dependent data broadcasting for unordered queries in a multiple channel mobile environment

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.     

不对称通信环境中动态广播调度算法分析

在数据传输模式中，服务器拥有比客户更强的通信传输能力的这类系统被称为不对称通信系统，如客户服务器模型与数据广播系统。这类伴随着网络、无线通信和移动计算等技术快速发展的系统都有其高效率的数据传输方式。其中广播系统包含着两种运行模式：pull—based的客户从服务器请求数据的模式和push—based的服务器直接将数据分发给大量客户的模式。这两种系统都需要高效的广播调度机制才能充分发挥系统的性能。分别讨论了pull—based的按需广播和push—based的主动广播的动态调度算法、性能参数指标及其性能特点。