Current approaches to complex event recognition have various tradeoffs. In this white paper, the benefits and limits of various CER approaches are discussed.
Complex event recognition, which makes use of pattern identification, is a crucial tool for making sense of real-time data. Use cases include traffic management and maritime surveillance, as well as detection of computer network attacks and financial fraud.
Current approaches to complex event recognition have tradeoffs, however, such as dealing with data velocity, obtaining background knowledge about objects, and handling uncertainty (download white paper).
What You Will Learn
Here’s what this white paper will cover:
- Sources of uncertainty in IoT and geospatial data.
- Requirements for complex event recognition systems.
- How current approaches in complex event recognition deal with probability, and the tradeoffs between automata-based and logic-based systems.
- Ways forward in improving handling of uncertainty in complex event recognition systems.
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