REVIEW ISSUES, TASKS & APPLICATIONS OF TEMPORAL DATA MINING IN IT INDUSTRIES
Keywords:
Spatiotemporal data mining, spatiotemporal data mining issues, spatiotemporal data mining tasksAbstract
Temporal Data Mining is a rapidly evolving area of research that is at intersection of several disciplines, including statistics, temporal pattern recognition, temporal databases, optimization, visualization, high-performance computing, & parallel computing. Spatiotemporal data usually contain states of an object, an event or a position within space over a period of time. Vast amount of spatiotemporal data can be found within several application fields such as traffic management, environment monitoring, & weather forecast. These datasets might be collected at different locations at various points of time within different formats. It poses many challenges within representing, processing, analysis & mining of such datasets because of complex structure of spatiotemporal objects & relationships among them in both spatial & temporal dimensions. In this research problems & challenges related to spatiotemporal data representation, analysis, mining & visualization of knowledge are presented. Several kinds of data mining tasks such as association rules, classification clustering for discovering knowledge from spatiotemporal datasets are examined & reviewed. System functional requirements for such kind of knowledge discovery & database structure are discussed. Finally applications of spatiotemporal data mining are presented. These applications are related to huge data of processed within IT industries.
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