Ayse Yasemin Seydim

Location dependent query processing

The advances in wireless and mobile computing allow a mobile user to perform a wide range of aplications once limited to non-mobile hard wired computing environments. As the geographical position of a mobile user is becoming more trackable, users need to pull data which are related to their location, perhaps seeking information about unfamiliar places or local lifestyle data. In these requests, a location attribute has to be identified in order to provide more efficient access to location dependent data, whose value is determined by the location to which it is related. Local yellow pages, local events, and weather information are some of the examples of these data. In this paper, we give a formalization of location relatedness in queries. We differentiate location dependence and location awareness and provide thorough examples to support our approach.

An architecture for location dependent query processing

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. We give an overview of the proposed architecture, examine the query processing issues and discuss the future work plans.

A location dependent benchmark with mobility behavior

Location dependent benchmarking with mobility behavior is a necessary step in the evolution of improvements of wireless and computing technology. The components needed for a mobile computing benchmark include specifications for data, queries, mobile unit behavior and execution guidelines. One of the most unique types of queries present in the mobile computing environment is a location dependent query where its result depends on the issue location. In this paper we describe the main features of a location dependent benchmark including the execution guidelines. While targeted to location dependent applications, it contains more general queries as well. As such, it can be viewed as the first benchmark targeted to the general mobile computing environment.

Data in your space [wireless access]

The wireless world of the future will disseminate data through both push and pull technologies. In this paper, we propose an architecture which facilitates global wireless access to data using both push and pull techniques. By combining these techniques on the wireless channels, a more efficient and effective data access paradigm is developed.

Location Leveling

Location based service LBS is an appealing technology in the pervasive mobile computing environment. In this environment, the answer to a location dependent query depends on the location of the mobile user. However, the location granularity to which the mobile unit is bound by a location service may differ from that stored in the content providers database. As a result, a location granularity mismatch occurs. The authors propose a general software architecture, location leveling, to solve this problem. As their layered location leveling solution is independent of the support provided by the wireless provider and the content provider, it is flexible enough to be used by any. The location leveling ll model can be implemented as an independent agent or broker in the middleware layer. The proposed approach is developed with solid theoretical foundation found in previous multidimensional data modeling studies.