Activity Based Data Management in Mobile Environment Using CART and ID3 Data Mining Techniques
Er. Satwant Kaur1, Er. Varinderjit Kaur2, Er. Gurpreet Singh3
1Er.Satwant Kaur, RIET Phagwar, India.
2Er.Varinderjit Kaur, Assistant Prof. RIET Phagwar, India.
3Er.Gurpreet Singh, Assistant Prof.& Head CSE STSSIET, Jalandhar, India.
Manuscript received on July 19, 2013. | Revised Manuscript received on August 09, 2013. | Manuscript published on August 30, 2013. | PP: 13-16 | Volume-2, Issue-6, August 2013. | Retrieval Number: F1928082613/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Mobile environments, CART, ID3, proactive data management, prefetching, pushing.