| Title |
Process Instance Query Language to Include Process Performance Indicators in DMN |
| Authors |
Miguel Perez-Alvarez, Jose , Teresa Gomez-Lopez, Maria , PARODY NÚÑEZ, MARÍA LUISA, Gasca, Rafael M. |
| External publication |
Si |
| Means |
Ieee International Enterprise Distributed Object Computing Conference Workshops-Edocw |
| Scope |
Proceedings Paper |
| Nature |
Científica |
| Publication date |
01/01/2016 |
| ISI |
000386577000032 |
| Abstract |
Companies are increasingly incorporating commercial Business Process Management Systems (BPMSs) as mechanisms to automate their daily procedures. These BPMSs manage the information related to the instances that flow through the model (business data), and recover the information concerning the process performance (Process Performance Indicators). Process Performance Indicators (PPIs) tend to be used for the detection of possible deviations of expected behaviour, and help in the post-mortem analysis and redesign by improving the goals of the processes. However, not only are PPIs important in terms of their ability to measure and detect a derivation, but they should also be included at decision points to make the business processes more adaptable to the process reality at runtime. In this paper, we propose a complete solution that allows the incorporation of the PPIs into decision tasks, following the Decision Model and Notation (DMN) standard, with the aim of enriching the decisions that can be taken during the process execution. Our proposal firstly includes an extension of the decision rule grammar of the DMN standard, by incorporating the definition and the use of a Process Instance Query Language (PIQL) that offers information about the instances related to the PPIs involved. In order to achieve this objective, a framework has also been developed to support the enrichment of process instance query expressions (PIQEs). This framework combines a set of mature technologies to evaluate the decisions about PPIs at runtime. As an illustration a real sample has been used whose decisions are improved thanks to the incorporation of the PPIs at runtime. |
| Keywords |
Business processes; Performance Indicators; Decision Model and Notation; Process Instance Query |
| Universidad Loyola members |
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