Location-Dependent Query Processing Benchmark

2011 ◽  
pp. 372-398
Author(s):  
Ayse Yasemin Seydim ◽  
Margaret H. Dunham

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.

2012 ◽  
Vol 532-533 ◽  
pp. 897-901
Author(s):  
Ming Jun Wei ◽  
Li Chun Xia ◽  
Jian Guo Jin ◽  
Qiu Hong Fan

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.


2009 ◽  
Vol 5 (3) ◽  
pp. 205-232 ◽  
Author(s):  
Zoubir Mammeri ◽  
Franck Morvan ◽  
Abdelkader Hameurlain ◽  
Nadhem Marsit

In recent years, mobile devices and applications achieved an increasing development. In database field, this development required methods to consider new query types like location-dependent queries (i.e. the query results depend on the query issuer location). Although several researches addressed problems related to location-dependent query processing, a few works considered timing requirements that may be associated with queries (i.e., the query results must be delivered to mobile clients on time). The main objective of this paper is to propose a solution for location-dependent query processing under soft real-time constraints. Hence, we propose methods to take into account client location-dependency and to maximize the percentage of queries respecting their deadlines. We validate our proposal by implementing a prototype based on Oracle DBMS. Performance evaluation results show that the proposed solution optimizes the percentage of queries meeting their deadlines and the communication cost.


2008 ◽  
Author(s):  
Khaleel Ur Rahman Khan ◽  
Rafi U. Zaman ◽  
A. Venugopal Reddy ◽  
K. Aditya Reddy ◽  
T. Sri Harsha

Author(s):  
J. Jayaputera

The idea of this article is based on the parallel indexing concept (Taniar & Rahayu, 2002) in which an indexed object residing in a BS is either fully, partially, or not replicated to others BSs. Therefore, every server contains either partial or all indexes of other servers. In our proposed approach, whenever the requested results return from neighboring cells, we append the resulting items to the current cell. This implies that when the next user sends a request, the current cell needs to look up its own index first to verify if the data is in its local storage. If the data is not present, the current server sends a request to the neighboring cells on behalf of the client; otherwise, the current server directly sends the requested query to the client. We have evaluated our proposed approach and showed that the access time can be reduced by a factor of two.The next section of this article describes some related work. We then describe our proposed work and the simulation model, and we compare the performance of our proposed technique to other techniques. Finally, we conclude the article and suggest future work.


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