Due to a wide variety of application potentials of association rules, the problem of association rule discovery has been studied for several years. Most previous work overlooks time components, which are usually attached to transactions in databases. This work addresses temporal issues of association rules. Three forms of interesting mining tasks for temporal association rules with certain constraints are identified, including the discovery of valid time periods during which association rules hold, the discovery of periodicities that association rules have, and the discovery of association rules with temporal features. The search techniques and algorithms for those individual tasks have been developed and implemented in a prototype system with an integrated query and mining interface (IQMI) and a temporal mining language (TML). The significance of such an system has been shown in three aspects. Firstly, data selection and sampling for different mining tasks are easy to achieve. Secondly, ad-hoc mining for different application requirements is possible to fulfill. Finally, data mining activities can be undertaken in an interactive and iterative process. The results of experiments show that many time-related association rules that would have been missed with traditional approaches can be discovered with the techniques and approaches presented in this paper.