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Activity Based Data Management in Mobile Environment Using CART and ID3 Data Mining Techniques

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Author(s): Er.Satwant Kaur, | Er.Varinderjit Kaur | Er.Gurpreet Singh,

Journal: International Journal of Engineering and Advanced Technology
ISSN 2249-8958

Volume: 2;
Issue: 6;
Start page: 13;
Date: 2013;
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Keywords: mobile environments | CART | ID3 | proactive data management | prefetching | pushing

ABSTRACT
Mobile clients feature increasingly sophisticated wirelessnetworking support that enables real-time information exchange with remote databases. Location-dependent queries, such as determining the proximity of stationary objects (e.g., restaurants and gas stations) are an important class of inquiries. We present a novel approach to support nearestneighbor queries from mobile hosts by leveraging the sharing capabilities of wireless ad-hoc networks. We illustrate how previous query results cached in the local storage of neighboring mobile peers can be leveraged to either fully or partially compute and verify spatial queries at a local host. The feasibility and appeal of our technique is illustrated through extensive simulation results that indicate a considerable reduction of the query load on the remote database. Furthermore, the scalability of our approach is excellent because a higher density of mobile hosts increases its effectiveness. Most users in a mobile environment are moving and accessing wireless services for the activities they are currently engaged in. We propose the idea of complex activity for characterizing the continuously [1] changing complex behavior patterns of mobile users. For the purpose of data management, a complex activity is modeled as a sequence of location movement, service requests, the co-occurrence of location and service, or the interleaving of all above. An activity may be composed of subactivities. Different activities may exhibit dependencies that affect user behaviors. We argue that the complex activity concept provides a more precise, rich, and detail description of user behavioral patterns which are invaluable for data management in mobile environments. Proper exploration of user activities has the potential of providing much higher quality and personalized services to individual user at the right place on the right time.
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