Ruihong Huang
ABSTRACT
Pattern matching is an important task in the field of Complex Event Processing (CEP). However, exact event pattern matching methods could suffer from low hit rate and loss for meaningful events identification due to the heterogeneous and dirty sources in the big data era. Since both events and patterns could be imprecise, the actual event trace may have different event names as well as structures from the pre-defined pattern. The low-quality data even intensifies the difficulty of matching. In this work, we propose to learn embedding representations for patterns and event traces separately and calculate their similarity as the scores for approximate matching.
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Digital Library
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Index Terms
- Approximate Event Pattern Matching over Heterogeneous and Dirty Sources
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