Title Decision-making support for input data in business processes according to former instances
Authors PÉREZ ÁLVAREZ, JOSE MIGUEL , PARODY NÚÑEZ, MARÍA LUISA, GÓMEZ LÓPEZ, MARÍA TERESA , MARTÍNEZ GASCA, RAFAEL , CERAVOLO, PAOLO
External publication No
Means Comp. Sci. Info. Sys.
Scope Article
Nature Científica
JCR Quartile 4
SJR Quartile 3
JCR Impact 1.17000
SJR Impact 0.35000
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85111015538&doi=10.2298%2fCSIS200522051P&partnerID=40&md5=dfdafa5ea35159a8c22817b87da613dc
Publication date 16/01/2021
ISI 000670316400012
Scopus Id 2-s2.0-85111015538
DOI 10.2298/CSIS200522051P
Abstract Business Processes facilitate the execution of a set of activities to achieve the strategic plans of a company. During the execution of a business process model, several decisions can be made that frequently involve the values of the input data of certain activities. The decision regarding the value of these input data concerns not only the correct execution of the business process in terms of consistency, but also the compliance with the strategic plans of the company. Smart decision-support systems provide information by analyzing the process model and the business rules to be satisfied, but other elements, such as the previous temporal variation of the data during the former executed instances of similar processes, can also be employed to guide the input data decisions at instantiation time. Our proposal consists of learning the evolution patterns of the temporal variation of the data values in a process model extracted from previous process instances by applying Constraint Programming techniques. The knowledge obtained is applied in a Decision Support System (DSS) which helps in the maintenance of the alignment of the process execution with the organizational strategic plans, through a framework and a methodology. Finally, to present a proof of concept, the proposal has been applied to a complete case study.
Keywords Business processes; Input Data; Decision-making support; Evolution Models of variables; Constraint Programming; Process Instance Compliance
Universidad Loyola members

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