Many biomedical research databases contain time-oriented data resulting from longitudinal, time-series and time-dependent study designs, knowledge of which is not handled explicitly by most data-analytic methods. To make use of such knowledge about research data, we have developed an ontology-driven temporal mining method, called ChronoMiner. Most mining algorithms require data be inputted in a single table. ChronoMiner, in contrast, can search for interesting temporal patterns among multiple input tables and at different levels of hierarchical representation. In this paper, we present the application of our method to the discovery of temporal associations between newly arising mutations in the HIV genome and past drug regimens. We discuss the various components of ChronoMiner, including its user interface, and provide results of a study indicating the efficiency and potential value of ChronoMiner on an existing HIV drug resistance data repository.