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Complete Event Trend Detection in High-Rate Event Streams
Proceedings of the 2017 ACM International Conference on Management of Data - SIGMOD '17
Event processing applications from financial fraud detection to health care analytics continuously execute event queries with Kleene closure to extract event sequences of arbitrary, statically unknown length, called Complete Event Trends (CETs). Due to common event sub-sequences in CETs, either the responsiveness is delayed by repeated computations or an exorbitant amount of memory is required to store partial results. To overcome these limitations, we define the CET graph to compactly encodedoi:10.1145/3035918.3035947 dblp:conf/sigmod/PoppeLAR17 fatcat:awukizg2ujavhjsc4tyhpwzlwi