How Effective is Stemming and Decompounding for German Text Retrieval?
Information Retrieval
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Ontology Matching
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
The temporal logic of programs
SFCS '77 Proceedings of the 18th Annual Symposium on Foundations of Computer Science
Mining taxonomies of process models
Data & Knowledge Engineering
Process Model Abstraction: A Slider Approach
EDOC '08 Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference
Fuzzy mining: adaptive process simplification based on multi-perspective metrics
BPM'07 Proceedings of the 5th international conference on Business process management
The ICoP Framework: identification of correspondences between process models
CAiSE'10 Proceedings of the 22nd international conference on Advanced information systems engineering
Similarity of business process models: Metrics and evaluation
Information Systems
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process compliance analysis based on behavioural profiles
Information Systems
Process diagnostics using trace alignment: Opportunities, issues, and challenges
Information Systems
Business process model abstraction: a definition, catalog, and survey
Distributed and Parallel Databases
Probabilistic optimization of semantic process model matching
BPM'12 Proceedings of the 10th international conference on Business Process Management
Simplifying discovered process models in a controlled manner
Information Systems
| Hi-index | 0.00 |
While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction most often try to abstract from the events in an automated way which does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach which aims to abstract an event log to the same abstraction level which is needed by the business. We use domain knowledge extracted from existing process documentation in order to automatically match events and activities. Our proposed abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in a case study with a German IT outsourcing company.