Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Maintaining knowledge about temporal intervals
Communications of the ACM
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ThemeRiver: Visualizing Theme Changes over Time
INFOVIS '00 Proceedings of the IEEE Symposium on Information Vizualization 2000
Arc Diagrams: Visualizing Structure in Strings
INFOVIS '02 Proceedings of the IEEE Symposium on Information Visualization (InfoVis'02)
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Connecting time-oriented data and information to a coherent interactive visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
prefuse: a toolkit for interactive information visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
Visual Analytics: Scope and Challenges
Visual Data Mining
Direct manipulation interfaces
Human-Computer Interaction
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
ActiviTree: Interactive Visual Exploration of Sequences in Event-Based Data Using Graph Similarity
IEEE Transactions on Visualization and Computer Graphics
Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison
IEEE Transactions on Visualization and Computer Graphics
Intelligent visualization and exploration of time-oriented data of multiple patients
Artificial Intelligence in Medicine
Intelligent selection and retrieval of multiple time-oriented records
Journal of Intelligent Information Systems
Visualization of Time-Oriented Data
Visualization of Time-Oriented Data
Interactive visualization for information analysis in medical diagnosis
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
Event-based concepts for user-driven visualization
Information Visualization
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Temporal Event Sequence Simplification
IEEE Transactions on Visualization and Computer Graphics
Visual Analytics for Model Selection in Time Series Analysis
IEEE Transactions on Visualization and Computer Graphics
TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data
IEEE Transactions on Visualization and Computer Graphics
Editorial: Foreword to the special section on visual analytics
Computers and Graphics
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Temporal Data Mining is a core concept of Knowledge Discovery in Databases handling time-oriented data. State-of-the-art methods are capable of preserving the temporal order of events as well as the temporal intervals in between. The temporal characteristics of the events themselves, however, can likely lead to numerous uninteresting patterns found by current approaches. We present a new definition of the temporal characteristics of events and enhance related work for pattern finding by utilizing temporal relations, like meets, starts, or during, instead of just intervals between events. These prerequisites result in MEMuRY, a new procedure for Temporal Data Mining that preserves and mines additional time-oriented information. Our procedure is supported by SAPPERLOT, an interactive visual interface for exploring the patterns. Furthermore, we illustrate the efficiency of our procedure presenting a benchmark of the procedure's run-time behavior. A usage scenario shows how the procedure can provide new insights.