Event Processing over Uncertain Data

Abstract

Events are the main input of event-based systems. Some events are generated externally and flow across distributed systems, while other events and their content need to be inferred by the event-based system itself. Such inference has a clear trade-off between inferring events with certainty, using full and complete information, and the need to provide a quick notification of newly revealed events. Timely event inference is therefore hampered by the gap between the actual occurrences of events, to which the system must respond, and the ability of event-based systems to accurately infer these events. This gap results in uncertainty and may be attributed to unreliable data sources (e.g., an inaccurate sensor reading), unreliable networks (e.g., packet drop at routers), the use of fuzzy terminology in reports (e.g., normal temperature) or the inability to determine with certainty whether a phenomenon has occurred (e.g., declaring an epidemic). In this chapter we present the state-of-the-art in event processing over uncertain data. We provide a classification of uncertainty in event-based systems, define a model for event processing over uncertain data, and propose algorithmic solutions for handling uncertainty.We also define, for demonstration purposes, a simple pattern language that supports uncertainty and detail open issues and challenges in this research area.