Autor:
Cabanillas, Cristina

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cristinacabanillas@us.es
cristina.cabanillas@wu.ac.at

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Cabanillas

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Cristina

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Vienna University of Economics and Business
Universidad de Sevilla, Spain
University of Seville, Spain
Vienna University of Economics and Business, Austria
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Mostrando 1 - 3 de 3
  • Artículo
    Board Mining: Understanding the Use of Board-Based Collaborative Work Management Tools
    Bravo, Alfonso; Cabanillas, Cristina; Peña, Joaquin; Resinas, Manuel. Actas de las XVIII Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2023), 2023-09-12.
    Board-Based Collaborative Work Management Tools (BBTs) like Trello and Microsoft Planner are widespread and massively used. Their use includes the management of projects, static information, or processes, which is achieved by assigning and moving cards through lists representing specific states, steps, or other classification criteria. BBTs are a flexible solution as boards, lists and cards can be changed by the user to adapt to new situations, e.g., changes in the processes or projects. However, understanding how a board is being used is challenging because what can be seen at a glance is a static snapshot of its current state. BBTs usually produce logs that capture all the activity that has taken place within the boards. In this paper, we leverage that data for mining BBT logs to understand how boards are used and evolve over time. The contribution is three-fold: (i) we characterize boards according to their components and the behavior detected based on their use during a specific time period; (ii) we detect structural changes in the boards, which may imply board redesigns, and visualize the evolution of the boards’ lists; and (iii) we define a set of metrics to assess relevant features of BBT boards, which enables the classification of the boards led by BBT design patterns. To validate the approach, we have conducted an empirical analysis with more than 60 real event logs and a use case.
  • Artículo
    A Query Language for Exploring Directly-Follows Graph Collections
    Salas-Urbano, María; Capitán-Agudo, Carlos; Cabanillas, Cristina; Resinas, Manuel. Actas de las XVII Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.
    Visualization tools are very useful for data exploration and offer a very intuitive way to look at interesting results in data analysis. In previous work, systems have been designed that, starting from a dataset, generate and work on sets of data visualizations and find those that show desired trends automatically, thus avoiding manual exploration of many visualizations by the user. One of the most used mechanisms to obtain interesting visualizations is the use of a query-based language. However, the use of these systems in process mining is not contemplated, which would be very useful to specifically find interesting results among multiple Directly-Follows Graphs (DFGs) extracted from event logs without carrying out the typical manipulation tasks and exploring multiple DFGs. We are interested in extending an existing query-based language, adapting it to process mining. The goal of is to automatically generate DFG collections and search for visualizations that contain patterns of interest according to some queries made by the users. Thus, interesting visualizations can be found by obtaining and comparing properties of sets of DFGs generated by the system. As an advantage, this approach allows users to obtain interesting results without the need to carry out a manual exploration of a large number of visualizations using the existing process mining tools. We have carried out the evaluation of our approach by solving a challenge provided in a BPI Challenge.
  • Artículo
    Recommending Interesting Results in Process Mining Analysis
    Capitán-Agudo, Carlos; Salas-Urbano, María; Cabanillas, Cristina; Resinas, Manuel. Actas de las XVII Jornadas de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.
    Identifying relevant insights in the results of process mining is challenging and time-consuming since it requires considerable knowledge and relies heavily on manual efforts to find adequate subsets of data with the insights. Current tools lack proper support to help the user in this manual process. To mitigate this, some approaches have been developed to provide guidance based on clustering traces and finding differences in basic Process Performance Indicators using statistical tests. However, some insights might not only be based on differences and they might require looking into other subsets of traces. In this paper, we make a proposal to guide users to find results with interesting insights in process mining analysis. It receives an event log and provides subsets of traces with interesting insights along with possible explanations about why they are interesting. We achieve that by using data mining measures of interestingness in process metrics. We illustrate the potential use of our proposal with a real use case.