Our Ph.D. student, Michael Arias, offered the presentation “Data Science and Process Mining” in the XXIV Congreso Nacional de Estudiantes de Ingenería de Sistemas y Computación (CONEISC 2016) in Pucallpa, Peru. During his visit Michael talk about the ideas behind the link between data science and process mining, available process mining tools, and also share some details about his research in the resource allocation topic.
The Process Mining UC research group congratulates Michael Arias, Jorge Munoz-Gama and Marcos Sepúlveda, for their accepted paper in the 1st workshop on Resource Management in Business Processes 2016 (ReMa’16), organized in conjunction with 14th International Conference on Business Process Management (BPM 2016), which will be held in the city of Rio de Janeiro between September 18th and 22nd of 2016.
The name of the accepted article is “A Multi-criteria Approach for Team Recommendation”.
General framework for Team Resource Recommendation
This work presents a novel approach that recommends the most suitable teams to execute a request defined at run-time. The approach considers four elements: (i) a resource request characterization, (ii) historical information on the process execution and expertise information, (iii) different metrics which calculate the suitability of the work teams taking into account both individual performance as well as collective performance of the resources, and (iv) a recommender system based on the Best Position Algorithm (BPA2) to obtain a ranking for the recommended work teams.
The Process Mining UC research group congratulates Michael Arias, Eric Rojas, Jonathan Lee, Jorge Munoz-Gama and Marcos Sepúlveda, for their accepted Demo paper in the 14th International Conference on Business Process Management (BPM2016), which will be held in the city of Rio de Janeiro between September 18th and 22nd of 2016.
The work presents the tool ResRec, a novel Multi-factor Criteria tool that can be used to recommend and allocate resources dynamically. The tool is a decision maker-oriented approach that provides the feature of solving individual requests (On-demand), or requests made in blocks (Batch) through a recommender system developed in ProM.
The authors must present their research and a poster at the at the BPM 2016 Demo Track.
Tool available here.
The group TECHNOLOGIES FOR DIGITAL LEARNING LAB congratulates Ronald Perez and Jorge Maldonado, who presented a publication in the 42nd Latin American Informatics Conference, CLEI 2016, which will be held in the city of Valparaiso between October 10th and October 14th of 2016.
The name of the accepted article is “How to design tools for supporting self-regulated learning in MOOCs? Lessons learned from a literature review from 2008 and 2016”.
This paper presents a systematic literature review that examines and analyzes the articles from 2008 until 2016 that have addressed the development of tools to support Self-Regulated Learning (SRL) in online and MOOC environments. The findings denote that: (1) there is a lack of tools to support SRL in MOOC environments; (2) the evaluation of the existing tools are not aligned whit the objectives of the research; (3) current research presents proposal of tools but very few achieve the stage of implementation; and (4) current existing tools tend to support many SRL strategies at the same time. Finally, it ends with a set of lessons learned for guiding the implementation of tools to support SRL strategies in MOOCs environments.
The authors Ronald Pérez (Department of Computer Science, Pontificia Universidad Católica de Chile, Chile and Universidad de Costa Rica, Headquarters Pacific, Costa Rica), Mar Pérez-Sanagustín (Department of Computer Science, Pontificia Universidad Católica de Chile, Chile) and Jorge Maldonado (Department of Computer Science, Pontificia Universidad Católica de Chile, Chile and Department of Computer Science, Universidad de Cuenca, Ecuador) must present their research at the conference in order to receive the feedback necessary within this event.