Process Mining Versus Intention Mining

@inproceedings{Khodabandelou2013ProcessMV,
  title={Process Mining Versus Intention Mining},
  author={Ghazaleh Khodabandelou and Charlotte Hug and R{\'e}becca Deneck{\`e}re and Camille Salinesi},
  booktitle={BMMDS/EMMSAD},
  year={2013}
}
Process mining aims to discover, enhance or check the conformance of activity-oriented process models from event logs. A new field of research, called intention mining, recently emerged. This field has the same objectives as process mining but specifically addresses intentional process models (processes focused on the reasoning behind the activities). This paper aims to highlight the differences between these two fields of research and illustrates the use of mining techniques on a dataset of… 

Literature Review about Intention Mining in Information Systems

TLDR
A literature review about the development of intention mining in the information systems engineering area is conducted to define the state of the art of the intention mining techniques that are have developed based on the process mining techniques.

From event logs to goals: a systematic literature review of goal-oriented process mining

TLDR
A systemic literature review based on 24 papers rigorously selected from four popular search engines in 2018 is provided to assess the state of goal-oriented process mining, highlighting that the use of process mining in association with goals does not yet have a coherent line of research, whereas intention mining shows a meaningful trace of research.

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TLDR
This paper mainly focuses and discusses on the literature review algorithms, models and tools used in Intention Mining, hoping that this information will be useful for developing models to retrieve intentions from the traces of activities and developing various intention mining techniques.

Intention-Oriented Process Model Discovery from Incident Management Event Logs

TLDR
An application of intention-oriented process mining for the domain of incident management of an Information Technology Infrastructure Library (ITIL) process, using the Map Miner Method (MMM) on a large real-world dataset for discovering hidden and unobservable user behavior, strategies and intentions.

A novel approach to process mining: Intentional process models discovery

TLDR
A novel approach of process mining, called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs, which includes two specific algorithms developed to infer users' intentions and construct intentional process model (Map) respectively.

Process Mining: intentional process model generation for recommendation

TLDR
This work aims to improve the conformance between a prescribed and the discovered process models, and to reuse the model for a better productivity and quality of the products.

Intentional Process Mining: Discovering and Modeling the Goals Behind Processes using Supervised Learning

TLDR
The paper presents the Supervised Map Miner Method and reports two controlled experiments that were undertaken to evaluate precision, recall and F-Score and the results are promising since the authors were able to find the intentions underlying the activities as well as the corresponding map process model with satisfying accuracy, efficiency and performance.

What Requirements Engineering can Learn from Process Mining

  • Mahdi Ghasemi
  • Computer Science
    2018 1st International Workshop on Learning from other Disciplines for Requirements Engineering (D4RE)
  • 2018
TLDR
How requirements engineering can benefit from process mining's components such as execution logs, process discovery and conformance techniques for requirements elicitation, prioritization and validation is highlighted.

Unsupervised discovery of intentional process models from event logs

TLDR
A novel approach, so-called Map Miner Method (MMM), designed to automate the construction of intentional process models from process logs, which offers a new understanding of software processes, and could readily be used for recommender systems.

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