What is it about?
the semantic fuzzy mining approach is capable not just for representing information in formats that can be easily understood by humans, but also for building applications/systems that trails to inclusively process the information that they contain or supports.
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Why is it important?
By the term semantic fuzzy mining (or better still - machine-understandable systems) - the extracted informations or models are either semantically labelled (annotated) to ease the analysis process, or represented in a formal structure (ontology) which allows a computer (e.g. the reasoner) to infer new facts by making use of the underlying relations.
Perspectives
The Semantic Fuzzy Mining technique shows that quality augmentation of events logs and process models is as a result of employing process mining methods that are capable of encoding the envisaged systems with the three rudimentary building blocks: - Semantic Labelling (annotation) - Semantic Representation (ontology) - Semantic Reasoning (reasoner) Thus “a system which is formally encoded with semantic labelling (annotation), semantic representation (ontology) and semantic reasoning (reasoner) has the capability to lift process mining results and analysis from the syntactic level to a much more conceptual level”.
Dr Kingsley Okoye
University of East London
Read the Original
This page is a summary of: Semantic fuzzy mining: Enhancement of process models and event logs analysis from syntactic to conceptual level, International Journal of Hybrid Intelligent Systems, December 2017, IOS Press,
DOI: 10.3233/his-170243.
You can read the full text:
Resources
Semantic-based process mining and analysis
Goal: The use of semantic-based approaches to manage perspectives of process mining. Emphasis on improving the information values and analysis of process models and event data logs. Project topic areas: Process mining, Semantics technologies, Analytical models, Process models, Ontologies, Real-time systems, Algorithm design and analysis, Event logs, Data models, Data mining, Semantic annotation, Learning process automation etc. Methods: Process mining tools and algorithms, Semantic modelling techniques and analysis
Semantic-based Process Mining and Model Analysis
Goal: The use of semantic-based approaches to manage perspectives of process mining. Emphasis on improving the information values and analysis of process models and event data logs. Project topic areas: Process mining, Semantics technologies, Analytical models, Process models, Ontologies, Real-time systems, Algorithm design and analysis, Event logs, Data models, Data mining, Semantic annotation, Learning process automation etc. Methods: Process mining tools and algorithms, Semantic modelling techniques and analysis
Contributors
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