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SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
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FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
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ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Point-Versus Interval-Based Temporal Data Models
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Mining Optimized Association Rules with Categorical and Numeric Attributes
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
SSDBM '96 Proceedings of the Eighth International Conference on Scientific and Statistical Database Management
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Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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Data mining with Temporal Abstractions: learning rules from time series
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ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
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Artificial Intelligence in Medicine
Mining frequent arrangements of temporal intervals
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IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
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ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
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ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
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Knowledge discovery from data sets can be extensively automated by using data mining software tools. Techniques for mining series of interval events, however, have not been considered. Such time series are common in many applications. In this paper, we propose mining techniques to discover temporal containment relationships in such series. Specifically, an item A is said to contain an item B if an event of type B occurs during the time span of an event of type A, and this is a frequent relationship in the data set. Mining such relationships provides insight about temporal relationships among various items. We implement the technique and analyze trace data collected from a real database application. Experimental results indicate that the proposed mining technique can discover interesting results. We also introduce a quantization technique as a preprocessing step to generalize the method to all time series.