Location-Dependent Query Processing: Semantic Cache for Real-Time Smart City Analytics

With the advent of wireless sensor networks and their deep integration with the world have enabled users worldwide to achieve benefits from location-based services through mobile applications, the problems such as low bandwidth, high network traffic, and disconnections issues are normally extracted from mobile services. An efficient database system is required to manage mentioned problems. Our research work finds the probability of user’s next locations. A mobile user (query issuer) changes its position when performing a specific mobile search, where these queries change and repeat the search with the issuer position. Moreover, the query issuer can be static and may perform searches with varying conditions of queries. Data is exchanged with mobile devices and questions that are formulated during searching for query issuer locations. An aim of the research work is achieved through effectively processing of queries in terms of location-dependent, originated by mobile users. Significant studies have been performed in this field in the last two decades. In this paper, our novel approach comprise of usage of semantic caches with the Bayesian networks using a prediction algorithm. Our approach is unique and distinct from the traditional query processing system especially in mobile domain for the prediction of future locations of users. Consequently, a better search is analyzed using the response time of data fetch from the cache.

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 provider’s 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.

Study on Clustering-Based Location Dependent Query

This paper firstly analyzes the importance and necessity of location dependent query in the mobile computing. Then, it proposes a special case in the application of the location dependent query. That is as follows: Inquirers may send the same location dependent query in different but similar positions. However, the server will not deal with them together but treat them separately. Thus, it will not only cause the waste of system resources but also delay disposal of other queries. According to the principal of clustering we propose a new location Analysis Algorithms-similar merging location analysis algorithm (SMLA). By the algorithm, similar queries can be combined into the same query, so as to reduce the load on central servers, improve system efficiency and query processing performance.

Location-Dependent Query Processing Benchmark

Benchmarks define techniques which can be followed to determine the effectiveness of a given software or hardware design. Ever since the development of the Wisconsin Benchmark and subsequent transaction-processing (TPC) benchmarks, there has been a concensus and general acceptance of these performance comparison tools. However, these benchmarks are not sufficient to determine the performance of mobile-based applications. For example, these traditional benchmarks ignore some of the important wireless-mobile features such as location-dependent queries and movement of the mobile host. In this chapter we examine the issues needed for the development of such a mobile query benchmark. In particular, we focus on queries which involve location-dependent features. We first examine the unique aspects of this mobile architecture which impact any benchmark design, and then propose a benchmark suitable for it.