Mining long sequential patterns in a noisy environment
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Pattern discovery in sequences under a Markov assumption
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-based sequential pattern mining: the pattern-growth methods
Journal of Intelligent Information Systems
Constructing comprehensive summaries of large event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing comprehensive summaries of large event sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
Do you know your IQ?: a research agenda for information quality in systems
ACM SIGMETRICS Performance Evaluation Review
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
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
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We present EventSummarizer - a tool for extracting comprehensive summaries from large event sequences. EventSummarizer takes as input a sequence with events of different types that occur during an observation period, and creates a partitioning of this time period into contiguous non-overlapping intervals such that each interval can be described by a simple model. Within each interval local associations between events of different types are reported. EventSummarizer runs on top of any Relational DataBase Management System (RDBMS), on tables with a timestamp attribute. Our system is parameter free and has a visual interface that provides the user with a global view of the input sequence via the segmentation of the timeline. The easy-to-use interface provides the user with the option to further examine the activity and associations of event types within each segment.