Frequent episode mining within the latest time windows over event streams

Abstract

With the wide use of EDGEs (electronic data gathering equipments) such as sensors and RFID (radio frequency identification) devices, unprecedented volumes of event streams have been generated. Mining frequent episodes within the latest time windows over event streams plays a significant role in event monitoring. It helps to generate episode rules, which can reflect the latest change, and predict future events effectively. The paper proposes how to mine MinEpi (minimal occurrence based frequent episode) within the latest time windows. The existing MinEpi mining methods are all Apriori-like, which need to scan data time after time and generate quantities of candidate episodes. This results in high time and space cost. Moreover, Apriori-like methods cannot be applied to event streams. For these problems, the paper proposes the episode matrix and frequent episode tree based mining method (EM&FET), which can generate frequent 2-episodes by constructing an episode matrix and generate higher-level frequent episodes directly by extending lower-level ones gradually, only scanning data once without candidate generation. Moreover, the paper further improves EM&FET, which enhances efficiency and saves space greatly. The experiments on different types of real data sets show the effectiveness and high efficiency of EM&FET and its improvement.