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A Data Mining Approach for Cache Replacement in Location-Dependent Environment

机译:位置相关环境中用于缓存替换的数据挖掘方法

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With the extensive popularity of mobile devices, a huge amount of research effort has been attempted towards exploring several aspects of mobile computing. In the light of many applications such as traffic conditions, stock information, weather reports and most importantly location dependent services - that require processing of location dependent queries - this area of research has gained much importance. A mobile server is expected to concurrently serve many clients. Periodic broadcasting is an important method for data dissemination in a mobile environment and is also independent of the number of users. Caching frequently accessed data items in location dependent environment for making adaptive updates, is a better method to exploit broadcasting strategy. The location dependent aspect along with the temporal aspect of the data, if applied intelligently in caching the data, is expected to give a better performance for accessing data and improving cache consistency. In this paper we intend to give, a method of predictive prefetching of data, obtained after processing the past references, to derive the probability of future access based on data mining techniques, that is expected to improve the performance of data retrieval in location-dependent environment thus providing efficient mobility management. This paper gives a data mining approach for performing cache replacement that gives accurate predictions and can also improve the system performance.
机译:随着移动设备的广泛普及,已经尝试了大量研究工作来探索移动计算的多个方面。鉴于交通状况,库存信息,天气报告以及最重要的依赖位置的服务(需要处理依赖位置的查询)等许多应用,这一研究领域变得非常重要。预计移动服务器可以同时为许多客户端提供服务。周期性广播是移动环境中数据分发的重要方法,并且与用户数量无关。在位置相关的环境中缓存经常访问的数据项以进行自适应更新是一种利用广播策略的更好方法。如果智能地将其应用于数据缓存中,则与位置有关的方面以及数据的时间方面都有望为访问数据和提高缓存一致性提供更好的性能。在本文中,我们打算提供一种经过预测的数据预取方法,该方法是在处理过去的参考文献之后获得的,用于基于数据挖掘技术得出未来访问的可能性,有望改善与位置相关的数据检索的性能。环境,从而提供有效的移动性管理。本文提供了一种用于执行高速缓存替换的数据挖掘方法,该方法可以提供准确的预测,还可以提高系统性能。

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