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Analysis of institutional authors

Rodriguez-Fernandez VAuthorGonzalez-Pardo AAuthorCamacho DAuthor

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December 7, 2020
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Conformance Checking for Time-Series-Aware Processes

Publicated to:IEEE Transactions on Industrial Informatics. 17 (2): 871-881 - 2021-02-01 17(2), DOI: 10.1109/TII.2020.2977126

Authors: Rodriguez-Fernandez V; Trzcionkowska A; Gonzalez-Pardo A; Brzychczy E; Nalepa GJ; Camacho D

Affiliations

AGH University of Science and Technology - Author
Universidad Autónoma de Madrid - Author
Universidad Politécnica de Madrid - Author
Universidad Rey Juan Carlos - Author
Uniwersytet Jagielloński w Krakowie - Author
‎ AGH Univ Sci & Technol, Fac Min & Geoengn, PL-30059 Krakow, Poland - Author
‎ AGH Univ Sci & Technol, PL-30059 Krakow, Poland - Author
‎ Jagiellonian Univ, PL-31007 Krakow, Poland - Author
‎ Univ Autonoma Madrid, Dept Comp Sci, Madrid 28049, Spain - Author
‎ Univ Politecn Madrid, Dept Sistemas Informat, Madrid 28040, Spain - Author
‎ Univ Rey Juan Carlos, Dept Comp Sci, Mostoles 28933, Spain - Author
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Abstract

© 2005-2012 IEEE. This article tackles the problem of checking the conformance between a business process model and the data produced by its execution in cases where the data are not given as an event log, but as a set of time series containing the evolution of the variables involved in the process. Tasks in the process model are no longer restricted to the occurrence of a single event, and instead, they can be expressed as a set of temporal conditions about the values of the variables in the log. This causes a paradigm shift in conformance checking (and process mining at a more general level), and because of this, the formalization of both the data and the process model and the algorithms are here redesigned and adapted for this challenging perspective. To illustrate the effectiveness of our approach, an experimental evaluation on a real-world time-series log is carried out, highlighting the benefits of this change of paradigm.

Keywords

Conformance checkingData miningIndexesInformaticsPetri netsProcess miningTask analysisTime seriesTime series analysisWorkflow net (wf-net)

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE Transactions on Industrial Informatics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 4/112, thus managing to position itself as a Q1 (Primer Cuartil), in the category Engineering, Industrial. Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.24, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-18, the following number of citations:

  • Scopus: 8

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-18:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 18.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 18 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.85.
  • The number of mentions on the social network X (formerly Twitter): 2 (Altmetric).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Poland.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (RODRIGUEZ FERNANDEZ, VICTOR) and Last Author (CAMACHO FERNANDEZ, DAVID).