Temporal Pattern Mining in Dynamic Environments

Many domains feature a dynamic characteristic like logistics, sports, or medicine and it would be useful to learn about frequent patterns, e.g., in what situations a traffic jam is likely to happen. Additionally, we are facing complex situations with many objects and relations that might change over time. The learning approach developed in this work identifies frequent temporal patterns out of qualitative, interval-based descriptions of dynamic scenes by extending the Apriori algorithm and ...