Declarative process modeling languages are especially suitable to model loosely-structured, unpredictable business processes. One of the most prominent of these languages is Declare. The Declare language can be used for all process mining branches and a plethora of techniques have been implemented to support process mining with Declare. However, using these techniques can become cumbersome in practical situations where different techniques need to be combined for analysis. In addition, the use of Declare constraints in practice is often hampered by the difficulty of modeling them: the formal expression of Declare is difficult to understand for users without a background in temporal logic, whereas its graphical notation has been shown to be unintuitive. In this paper, we present RuM, a novel application for rule mining that addresses the above-mentioned issues by integrating multiple Declare-based process mining methods into a single unified application. The process mining techniques provided in RuM strongly rely on the use of Declare models expressed in natural language, which has the potential of mitigating the barriers of the language bias. The application has been evaluated by conducting a qualitative user evaluation with eight process analysts.
Rule Mining with RuM / Alman, Anti; Di Ciccio, Claudio; Haas, Dominik; Maggi, Fabrizio Maria; Nolte, Alexander. - (2020), pp. 121-128. ((Intervento presentato al convegno 2nd International Conference on Process Mining, ICPM 2020 tenutosi a Padua, Italy.
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|Titolo:||Rule Mining with RuM|
|Data di pubblicazione:||2020|
|Citazione:||Rule Mining with RuM / Alman, Anti; Di Ciccio, Claudio; Haas, Dominik; Maggi, Fabrizio Maria; Nolte, Alexander. - (2020), pp. 121-128. ((Intervento presentato al convegno 2nd International Conference on Process Mining, ICPM 2020 tenutosi a Padua, Italy.|
|Appartiene alla tipologia:||04b Atto di convegno in volume|