Data uncertainty is a common problem for the real-time monitoring of data streams. In this paper, we address the issue of efficiently monitoring the satisfaction/violation of user-defined constraints over data streams where the data uncertainty can be probabilistically characterized. We propose a monitoring architecture SPMON that can incorporate probabilistic models of uncertainty in constraint monitoring. We adapt the concept of data similarity in real-time databases to the processing of uncertain data streams. In doing so, we generalize the data similarity by a new concept psr (probabilistic similarity region) that allows us to define similarity relations for probabilistic data with respect to the set of constraints being monitored. This enables the construction of lightweight filters for saving bandwidth. We also show how to efficiently update the filter conditions at run-time.
['Honguk Woo', 'Aloysius K. Mok']
Real-Time Monitoring of Uncertain Data Streams Using Probabilistic Similarity