Discovery of temporal knowledge from databases

In this investigation, we discuss discovery technique of temporal knowledge from databases. We extract interval hierarchy from the database as well as association rules over the hierarchy, which in turn, we could understand the results in an easier manner. Our basic idea comes from mining association rules. Given minimum unit of intervals, we try to obtain useful rules in a sense of support and confidence, and we merge intervals when we get more useful results. Eventually we get to the efficient rules and the interval hierarchy to reason the set of rules.