EFFECTIVE AND EFFICIENT MINING OF DATA IN MOBILE COMPUTING.

Data Mining consists of an evolving set of techniques that can be used to extract valuable Information and knowledge from massive volumes of data. Data Mining Research and tools have focused on commercial sector applications. This Research paper highlights the data mining techniques applied to mine for Location and Mobility Management. Location Management is a very important and complex problem in today's mobile computing environments. There is a need to develop algorithms that could capture this complexity, yet can be easily implemented and used to solve a wide range of Location Management scenarios. In the Reporting cell Location Management scheme, the mapping is done on the basis of classification. Some cells in the network are designated as Reporting cells; Mobile terminals update their positions (Location Update) upon entering one of these reporting cells. The remaining cells are designated as Non-reporting cells. In the proposed scheme, an supervised learning technique is being introduced which maintains the history or mobility pattern (of size h) of the last visited reporting cell. The updation does not take place, when the user roams with in the reporting cells of his mobility pattern. The location management is updated when the user enters in to the new reporting cell, which is not in his history. As a result the updation cost is proportionately reduced with the value of h (the number of entries in the history) Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from the capability in searching large search spaces, which arise in many combinatorial optimization problems very efficiently. To create such a planner, Genetic Algorithm has been implemented to show that the total cost is less when compared with the existing cost based updation and searching scheme. [ABSTRACT FROM AUTHOR]