We consider the mining of both a single event history and two correlated event histories. We first propose a method for mining binary relationships from a single event history. The binary relationships among events are used to organize the events into related groups of event. This organization is important because the number of events in an event-driven system may become very high and unmanageable. The groups of related events and the relationships among the events are exploited for predictive event detection and proactive rule execution in active database systems. We also consider the mining of two correlated event histories which are disjoint and the events in one history are related to the events in the other history. We describe how we can efficiently extract associations among the events spanning different event histories, which we call cross associations.

We have chosen data broadcast in mobile computing environments as a case study for active data management. One of the important facts in mobile computing environments with wireless communication medium is that the server-to-client (downlink) communication bandwidth is much higher than the client-to-server (uplink) communication bandwidth. This asymmetry makes the dissemination of data to client machines a desirable approach. However, the dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. Our purpose is to show how active data management can be used to improve mobile data management through broadcast data organization and prefetching from the broadcast medium. In order to achieve this, the client requests of data items at the server are considered as events and the chronological sequence of items that have been requested by clients is considered as an event history. Event history in broadcast medium is called a broadcast history. The first step in our work is to analyze the broadcast history to discover sequential patterns describing the client access behavior. The sequential patterns are used to organize the data items in the broadcast disk in such a way that the items requested subsequently are placed close to each other. Then, we utilize the predictive event detection techniques to improve the cache hit ratio to be able to decrease the access latency. Prediction of future client access behavior enables clients to prefetch the data from the broadcast disk based on the rules extracted from the broadcast history.