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Data mining is employed to generate rules that can be used as the basis for decision making. Sequence mining can be stated as the discovery of inter- transaction associations or in data stored in temporal datasets, as a series of events, or episodes, occurring at specific times. While the mining of frequent episodes is important, the manner in which such episodes interact can provide further useful knowledge. Until now these patterns have been based on the endpoints of the intervals, however in many cases the natural point of reference is the midpoint of an interval and it is therefore appropriate to develop a mechanism for reasoning between intervals when this information is known. This book presents a method for discovering interacting episodes from temporal sequences and analysis of them using temporal patterns. Mining can be conducted both with and without the mechanism for handling the pace of events and the analysis is conducted using both the traditional interval algebras and a midpoint algebra presented in this book. This book should be useful to data miners, especially those interested in sequential pattern mining.