Workflow handbook 1997
Workflow management: models, methods, and systems
Workflow management: models, methods, and systems
On the discovery of process models from their instances
Decision Support Systems
Mining Process Models from Workflow Logs
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Automating the Discovery of As-Is Business Process Models: Probabilistic and Algorithmic Approaches
Information Systems Research
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Discovery of temporal patterns from process instances
Computers in Industry - Special issue: Process/workflow mining
Discovering Frequent Arrangements of Temporal Intervals
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining sequences with temporal annotations
Proceedings of the 2006 ACM symposium on Applied computing
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Journal of the American Society for Information Science and Technology
Mining unconnected patterns in workflows
Information Systems
Mining Clinical Data with a Temporal Dimension: A Case Study
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Time-Annotated Sequences for Medical Data Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Mining relationships among interval-based events for classification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Mining sequences of temporal intervals
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Learning pattern graphs for multivariate temporal pattern retrieval
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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In the past few years there has been an increasing interest in the analysis of process logs. Several proposed techniques, such as workflow mining, are aimed at automatically deriving the underlying workflow models. However, current approaches only pay little attention on an important piece of information contained in process logs: the timestamps, which are used to define a sequential ordering of the performed tasks. In this work we try to overcome these limitations by explicitly including time in the extracted knowledge, thus making the temporal information a first-class citizen of the analysis process. This makes it possible to discern between apparently identical process executions that are performed with different transition times between consecutive tasks. This paper proposes a framework for the user-interactive exploration of a condensed representation of groups of executions of a given process. The framework is based on the use of an existing mining paradigm: Temporally-Annotated Sequences (TAS). These are aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data. With the extracted TAS, which represent sets of possible frequent executions with their typical transition times, a few factorizing operators are built. These operators condense such executions according to possible parallel or possible mutual exclusive executions. Lastly, such condensed representation is rendered to the user via the exploration graph, namely the Temporally-Annotated Graph (TAG). The user, the domain expert, is allowed to explore the different and alternative factorizations corresponding to different interpretations of the actual executions. According to the user choices, the system discards or retains certain hypotheses on actual executions and shows the consequent scenarios resulting from the coresponding re-aggregation of the actual data.