Mobility is the key in a globalized world where people, goods, data and even ideas move in increasing speeds over increasing distances. For representing a particular moving object in a database, examining the position of the moving object would be in focus. To analyze the particular, we create a framework so that it can represent the relevant moving object and its position over the time. Since moving object data is tested in several different real data sets, it will benefit moving objects data owners to carry out various kinds of analysis on their data. Thus, we relate the whole moving object data mining with granular computing to make a flexible and scalable analysis of targeted moving object data. Granular Computing provides a conceptual framework for studying many issues in data mining for moving object databases. This paper examines those issues, including mining object data and related knowledge representation and processing. It is demonstrated that one of the fundamental task of data mining is searching for the right level of granularity in moving object data and knowledge representation. On that basis, it makes the granules in the process of problem solving for modelling the human thinking process.

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Moving Object Databases Granular Computing Data Mining