Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Finding simple intensity descriptions from event sequence data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting duality in summarization with deterministic guarantees
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing comprehensive summaries of large event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Concept Clustering of Evolving Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Finding semantics in time series
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Efficient event stream processing: handling ambiguous events and patterns with negation
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications
Proceedings of the 20th ACM international conference on Information and knowledge management
LogSig: generating system events from raw textual logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Understanding user behavior through summarization of window transition logs
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
An integrated framework for optimizing automatic monitoring systems in large IT infrastructures
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently, much study has been directed toward summarizing event data, in the hope that the summary will lead us to a better understanding of the system that generates the events. However, instead of offering a global picture of the system, the summary obtained by most current approaches are piecewise, each describing an isolated snapshot of the system. We argue that the best summary, both in terms of its minimal description length and its interpretability, is the one obtained with the understanding of the internal dynamics of the system. Such understanding includes, for example, what are the internal states of the system, and how the system alternates among these states. In this paper, we adopt an algorithmic approach for event data summarization. More specifically, we use a hidden Markov model to describe the event generation process. We show that summarizing events based on the learned hidden Markov Model achieves short description length and high interpretability. Experiments show that our approach is both efficient and effective.