Knowledge Discovery in Temporal Databases
نویسنده
چکیده
The essence of data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Existing data mining tools consider snapshots of data and therefore unable to handle the complexity of a dynamic environment, such as financial applications which contain a huge amount of data that changes over time. The knowledge discovered has limited value since the temporal nature of data is not taken into account but only the current or latest snapshot. In this paper, we present our framework for data mining in temporal databases. We believe that the next-generation database systems and in particular those that accommodate temporal features are most appropriate platform for knowledge discovery.
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