Sequential learning for case-based pattern recognition in complex event domains

UK Workshop on Case-Based Reasoning 2011.

社会科学（总论）;计算机科学

Pablo Gay1 ; Beatriz López1 ; Joaquim Meléndez1

Others  :  http://ceur-ws.org/Vol-829/paper6.pdf
PID  :  41820

来源: CEUR

【 摘 要 】

Large and distributed environments usually generate huge amounts of information. The easiest way to deal with this information in an uncoupled and asynchronous way is using eventoriented approaches. These systems are usually implemented to react to the generated information.This paper presents a new track to add to these architectures a mechanism to discover behaviorscombined with a reasoning method that predicts the next most probable event. Consequently, thework focuses in two fields: sequence pattern mining and case-based reasoning. The former aims tocompress the original large amount of event data by discovering frequent behaviors in the form ofsequence patterns. The latteris used to recognize the behaviors and forecast future predictions basedon the learnt patterns. The methodology has been tested using real data from a public bike hiringsystem.