Discovering during-temporal patterns (DTPs) in large temporal databases

Large temporal databases (TDBs) usually contain a wealth of data about temporal events. Aimed at discovering temporal patterns with during relationship (during-temporal patterns, DTPs), which is deemed common and potentially valuable in real-world applications, this paper presents an approach to finding such DTPs by investigating some of their properties and incorporating them as desirable pruning strategies into the corresponding algorithm, so as to optimize the mining process. Results from synthetic reveal that the algorithm is efficient and linearly scalable with regard to the number of temporal events. Finally, we apply the algorithm into the weather forecast field and obtain effective results.

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