Online Frequent Episode Mining

• Published in: 2015 IEEE 31st International Conference on Data Engineering pp. 891 - 902
• Main Authors: Xiang Ao, Ping Luo, Chengkai Li, Fuzhen Zhuang, Qing He
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2015
• Subjects: Complexity theory; Data mining; Data structures; Finite element analysis; Time factors; Time-frequency analysis
• ISSN: 1063-6382; 2375-026X
• DOI: 10.1109/ICDE.2015.7113342

Frequent episode mining is a popular framework for discovering sequential patterns from sequence data. Previous studies on this topic usually process data offline in a batch mode. However, for fast-growing sequence data, old episodes may become obsolete while new useful episodes keep emerging. More importantly, in time-critical applications we need a fast solution to discovering the latest frequent episodes from growing data. To this end, we formulate the problem of Online Frequent Episode Mining (OFEM). By introducing the concept of last episode occurrence within a time window, our solution can detect new minimal episode occurrences efficiently, based on which all recent frequent episodes can be discovered directly. Additionally, a trie-based data structure, episode trie, is developed to store minimal episode occurrences in a compact way. We also formally prove the soundness and completeness of our solution and analyze its time as well as space complexity. Experiment results of both online and offline FEM on real data sets show the superiority of our solution.