A location dependent benchmark with mobility behavior

Information Sciences, 2000

In a mobile computing environment, a user with a wireless connection to the information network can access data via submitting queries to data servers. As the mobility is the most distinguishing feature of the mobile computing paradigm, location becomes an important piece of information for the so-called location-dependent queries, where the answer to a query depends on the current location of the user who issued the query. A location-dependent query submitted by a mobile user can become more dicult to process when it is submitted as a continuous query (CQ) for which the answer changes as the user moves. The answer to a location-dependent CQ can be provided as a set of tuples hSY beginY endi indicating that object S is the answer of the query from time begin to time end. Once the tuples in the answer set are determined, the next step is to determine the transmission time of these tuples to the user. Transmission time of tuples is critical in the sense that it can have a considerable impact on both the communication overhead imposed on the wireless network and the availability of tuples in case of disconnections. In this paper, we propose three tuple transmission approaches that can be used to determine the transmission time of tuples in the answer set of a location-dependent CQ. We also evaluate the relative performance of the proposed approaches under dierent settings of environmental parameters. Ó

2003

The usage of mobile equipment like PDAs, mobile phones, Tablet PCs or laptops is already common in our current information society. Typically, mobile information systems work in a context dependent (e.g. location dependent) manner. Considering this characteristics, we present a cache which uses trie-based indexing for the optimization of accessing data cached on the mobile devices.

IEEE Transactions on Knowledge and Data Engineering, 2005

With the cutting edge technology advance in wireless and mobile computers, the query processing in a mobile environment involves join processing among different sites which include static servers and mobile computers. Because of the need for energy saving and also the presence of asymmetric features in a mobile computing environment, the conventional query processing for a distributed database cannot be directly applied to a mobile computing system. In this paper, we first explore three asymmetric features of a mobile environment. Then, in light of these features, we devise query processing methods for both join and query processing. Intuitively, employing semijoin operations in a mobile computing environment is able to further reduce both the amount of data transmission and energy consumption. A semijoin which is initiated by a mobile computer (respectively, the server) and is beneficial to reduce the cost of a join operation is termed a mobile-initiated or MI (respectively, server-initiated or SI) profitable semijoin. According to those asymmetric features of a mobile computing system, we examine three different join methods and devise some specific criteria to identify MI/SI profitable semijoins. For query processing, which refers to the processing of multijoin queries, we develop three query processing schemes. In particular, we formulate the query processing in a mobile computing system as a two-phase query processing procedure that can determine a join sequence and interleave that join sequence with SI profitable semijoins to reduce both the amount of data transmission and energy consumption. Performance of these join and query methods is comparatively analyzed and sensitivity analysis on several parameters is conducted. Furthermore, we develop a systematic procedure to derive the characteristic functions of MI and SI profitable semijoins. It is noted that, given some system parameters, those characteristic functions are very important in determining which join method is the most appropriate one to employ in that configuration. It is shown by our simulation results that, by exploiting the three asymmetric features, these characteristic functions are very powerful in reducing both the amounts of energy consumption and data transmission incurred and can lead to the design of an efficient and effective query processing procedure for a mobile computing environment. Index Terms-Mobile database, mobile computing, query processing, join method. ae 1 INTRODUCTION I N a mobile computing environment, a mobile user with a power-limited palm computer (or a mobile computer) can access various information via wireless communication. Applications such as stock activities, traffic reports, and weather forecasts have become increasingly popular in recent years [26], [27]. It is noted that mobile computers use small batteries for their operations without directly connecting to any power source and the bandwidth of wireless communication is, in general, limited. As a result, an important design issue in a mobile system is to conserve the energy and communication bandwidth of a mobile unit while allowing mobile users the ability to access information from anywhere at anytime [3], [10], [18]. Various wireless data networking technologies, including IS-136 [24], CDMA2000 [19], and Wireless Application Protocol (WAP) [28], have been developed recently. Among

Mobile Information Systems, 2005

The emergence of mobile computing provides the ability to access information at any time and place. However, as mobile computing environments have inherent factors like power, storage, asymmetric communication cost, and bandwidth limitations, efficient query processing and minimum query response time are definitely of great interest. This survey groups a variety of query optimization and processing mechanisms in mobile databases into two main categories, namely: (i) query processing strategy, and (ii) caching management strategy. Query processing includes both pull and push operations (broadcast mechanisms). We further classify push operation into on-demand broadcast and periodic broadcast. Push operation (on-demand broadcast) relates to designing techniques that enable the server to accommodate multiple requests so that the request can be processed efficiently. Push operation (periodic broadcast) corresponds to data dissemination strategies. In this scheme, several techniques to improve the query performance by broadcasting data to a population of mobile users are described. A caching management strategy defines a number of methods for maintaining cached data items in clients' local storage. This strategy considers critical caching issues such as caching granularity, caching coherence strategy and caching replacement policy. Finally, this survey concludes with several open issues relating to mobile query optimization and processing strategy.

Computing Research Repository, 2010

The rapidly expanding technology of mobile communication will give mobile users capability of accessing information from anywhere and any time. The wireless technology has made it possible to achieve continuous connectivity in mobile environment. When the query is specified as continuous, the requesting mobile user can obtain continuously changing result. In order to provide accurate and timely outcome to requesting mobile user, the locations of moving object has to be closely monitored. The objective of paper is to discuss the problem related to the role of personal and terminal mobility and query processing in the mobile environment.

Performance and scalability of location-based services (LBSs) are crucial to the wide deployment of mobile enterprise systems. Location queries are a fundamental capability of LBSs. Conventional approaches to location query processing have been centered on an object-centric architecture where static and moving objects are processed under a unified framework through an object index or query index. In this paper, we identify and exploit the performance benefit of a location-centric framework by promoting a clean separation of location query processing over static objects from query processing over moving objects. We show the performance benefits of treating locations as first class citizens instead of objects. Concretely, we develop a federated location indexing scheme for processing location queries over static objects and maintain a grid-based object index for processing queries over moving objects. Our experimental results demonstrate that the location-centric framework enables highly efficient processing of different types of location queries in a mobile environment, prevails under all types of query workloads compared to the object-centric approaches, and in some scenarios it requires as low as only 6% of the IOs required by a corresponding object index for query evaluation.

Proceedings Seventh International Conference on Database Systems for Advanced Applications DASFAA 2001 DASFAA-01, 2001

Recent advances in mobile computing and mobile communication technology have led to the emergence of many innovative mobile-computing applications. Some of them require providing support to location-dependent continuous queries ( LDCQs) on moving objects. The result of a location dependent query depends on the current locations of the moving objects. When the query is specified as continuous, the requesting client can get continuously changing result. In order to provide correct and timely results to requesting clients, the locations of moving objects have to be closely monitored. In this paper, we propose an adaptive monitoring method (AMM) for managing the locations of moving objects to maintain the correctness of the results of query evaluation without significantly increasing the wireless bandwidth requirements. Extensive simulation experiments have been conducted to investigate the performance of the proposed method as compared to the plain dead-reckoning (pdr).

12th International Workshop on Database and Expert Systems Applications

As the mobility of people increases, anytime, anywhere computing has become a must. With the advances in wireless communications, the geographical position of the mobile user can be estimated accurately enough to be used to access data dependent on it. Location awareness then enables new kinds of services specific to localized information. While this provides great flexibility for the end user, it creates many challenges for the content providers and wireless operators. We propose an architecture, Location Dependent Services Manager (LDSM), between the mobile user and the service/content providers to service Location Dependent applications which access Location Dependent Data. The proposed middleware does location translation which we call Location Leveling, to adjust the location granularities and to solve the location mismatch problem. Quality of query results and the complicated queries involving more than one service provider is also supported in this middleware. In this paper, we give an overview of the proposed architecture, examine the query processing issues and discuss the future work plans.

2005

These last years, the evolution of nomadic terminals and mobile networks has yield to the development of the ubiquitous computing. In this context, actual query evaluation and optimization techniques in distributed databases based on the use of a global schema and a cost model are no more relevant. Furthermore, a query processor deployed in this type of environment must face a significant heterogeneity, in particular mobile terminals, communication networks and different data sources. In order to cope with this heterogeneity, such service should have the capability to adapt itself dynamically. In this paper, we describe the problems related to query processing in mobile environment and the needs for adaptability.

Abstract—Use of location based services has been gaining popularity over the years especially with the increase in smartphone sales. The benefits include helping you find nearby places of interest like a restaurant, café, atm among others and additional information like location and distance and providing navigation assistance to these places. In most cases such services are dependent on the availability of an internet connection on the phone.