On a class of O(n2) problems in computational geometry
Computational Geometry: Theory and Applications
A coding approach to event correlation
Proceedings of the fourth international symposium on Integrated network management IV
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining Mutually Dependent Patterns
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding an o(n2 log n) Algorithm Is Sometimes Hard
Proceedings of the 8th Canadian Conference on Computational Geometry
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Reliable Detection of Episodes in Event Sequences
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Temporal Patterns Without Predefined Time Windows
ICDM '04 Proceedings of the Fourth IEEE International Conference on 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
An integrated framework on mining logs files for computing system management
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
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the VLDB Endowment
Temporal Data Mining
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining console logs for large-scale system problem detection
SysML'08 Proceedings of the Third conference on Tackling computer systems problems with machine learning techniques
Learning Maximum Lag for Grouped Graphical Granger Models
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
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
Searching similar segments over textual event sequences
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Time lag is a key feature of hidden temporal dependencies within sequential data. In many real-world applications, time lag plays an essential role in interpreting the cause of discovered temporal dependencies. Traditional temporal mining methods either use a predefined time window to analyze the item sequence, or employ statistical techniques to simply derive the time dependencies among items. Such paradigms cannot effectively handle varied data with special properties, e.g., the interleaved temporal dependencies. In this paper, we study the problem of finding lag intervals for temporal dependency analysis. We first investigate the correlations between the temporal dependencies and other temporal patterns, and then propose a generalized framework to resolve the problem. By utilizing the sorted table in representing time lags among items, the proposed algorithm achieves an elegant balance between the time cost and the space cost. Extensive empirical evaluation on both synthetic and real data sets demonstrates the efficiency and effectiveness of our proposed algorithm in finding the temporal dependencies with lag intervals in sequential data.