Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
Distributed and Parallel Databases
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of frequent episodes from complex sequences
Information Systems
Efficient Mining of Contrast Patterns and Their Applications to Classification
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
CAMS: OLAPing Multidimensional Data Streams Efficiently
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Efficient mining of minimal distinguishing subgraph patterns from graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Mining closed episodes from event sequences efficiently
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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Existing studies on episode mining mainly concentrate on the discovery of (global) frequent episodes in sequences. However, frequent episodes are not suited for data streams because they do not capture the dynamic nature of the streams. This paper focuses on detecting dynamic changes in frequencies of episodes over time-evolving streams. We propose an efficient method for the online detection of abrupt emerging episodes and abrupt submerging episodes over streams. Experimental results on synthetic data show that the proposed method can effectively detect the defined patterns and meet the strict requirements of stream processing, such as one-pass, real-time update and return of results, plus limited time and space consumption. Experimental results on real data demonstrate that the patterns detected by our method are natural and meaningful. The proposed method has wide applications in stream monitoring and analysis as the discovered patterns indicate dynamic emergences/disappearances of noteworthy events/phenomena hidden in the streams.