Cache prefetch and replacement with dual valid scopes for location dependent data in mobile environments

Proceedings of 3rd International Conference on Data Management Technologies and Applications

Othman

et al. 2014

Pre-fetching is used to predict next query of data items before any problems occur due to network congestion, delays, and latency problems. Lately, pre-fetching strategies become more complicated in which to support new types of application especially for mobile devices. Sometime the pre-fetched data items are not interested to the users. Due to this complication, an intelligent technique is introduced where an integrated measurement using data mining with Bayesian approach is proposed to improve the query performance. In previous study, the pre-fetched data items were filtered using data driven measurement. The data was generated based on the data frequency metrics whereby the structure of the query pattern is quantified using statistical methods. The measurement is not good enough to solve sequence query in mobile environment. In this paper, a new technique is proposed to generate new and potential pre-fetching set for the users. A subjective measurement is used to determine the pre-fetching set based on user interestingness. The integrated measurement generates strong and weak association rules based on the data and user interestingness criterions. The result shows that the performance is significantly improved whereby the technique managed to quantify the uncertainty of users' expectation in the next possible query.

Section: Introductionmentioning

confidence: 99%

Integrated Measurement for Pre-Fetching in Mobile Environment

Proceedings of 3rd International Conference on Data Management Technologies and Applications

Othman

et al. 2014

User interestingness for pre-fetching in mobile environment

ICIMU 2011 : Proceedings of the 5th International Conference on Information Technology &Amp; Multimedia

Othman

et al. 2011

In mobile environment, pre-fetching is used to prevent network congestion, delays and latency problems during disconnection. Lately, the pre-fetching strategies become more complicated in which to support new types of application especially in wireless environments. Due to this complication, new method is introduced where it requires data mining technique to improve the pre-fetching process. Previously, the pre-fetched data item is filtered using an data driven measurement for data interestingness and the data generated are based on the structure of a query pattern and quantified using statistical methods. The measurement is not good enough to solve the rule quality problems as in predicting next query in mobile environment. In this paper, a new measurement method is proposed to generate new criterion of pre-fetched data items before sending to mobile users. This method is improved by introducing user driven measurement for user interestingness where it will consider the subjectivity and the understandability of the users.

New prediction model for pre-fetching in mobile database

Proceedings of the 12th International Conference on Information Integration and Web-Based Applications &Amp; Services

2010

In mobile environment, pre-fetching method is used to prevent network congestion, delays and latency problems and to improve data availability during disconnection. Many pre-fetching strategies have been introduced. Lately, the pre-fetching techniques in mobile systems become more complicated in which to support new types of applications such as in wireless environments. Due to this complication, researchers start to introduce new technique where it requires data mining technique to improve the situation in involuntary disconnection. Previously, the data is filtered using an objective measurement where data are generated based on the structure of a query pattern and quantified using statistical methods. The measures are not good enough to solve the rule quality problems as in answering query in mobile environment. In this study, a new prediction model is proposed to generate data item for additional criterion before sending to mobile users. This new criterion of data is measured by a subjective measurement which is based on the subjectivity and the understandability of the users who examine the query patterns. Therefore, it is expected that mobile users can make use and access the data items that are beyond their expectation to proceed with their job further without any problem.