Process Instance Query Language to Include Process Performance Indicators in DMN

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 ap plying 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.

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“…- [33]. The types of operations that can be performed over processes and task instances can be seen in Table 1.…”

Section: Dictionary (D)mentioning

confidence: 99%

Decision-making support for input data in business processes according to former instances

Pérez-Álvarez¹,

Parody

López

et al. 2021

COMSIS J

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“…Furthermore, in this stage, the predefined targets of the indicators are compared with the actual values (Sánchez González et al 2010), the predefined analysis models and decision criteria are applied to support the management team to analyze the process performance (Perez-Alvarez et al 2016), the quantitative management (Hikichi et al 2006;X. Wang, Ren, and Liu 2017), and in-process control.…”

Section: Process Monitoring and Analysismentioning

confidence: 99%

An MDA Proposal To Integrate the Measurement Lifecycle Into the Process Lifecycle

Meidan

2021

JWE

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Context: Measuring the Software Development Process (SDP) supports organizations in their endeavor to understand, manage, and improve their development processes and projects. In the last decades, the SDP has evolved to meet the market needs and keep abreast of modern technologies and infrastructures. These changes in the development processes have increased the importance of the measurement and caused changes in the measurement process and the used measures. Objective: This work aims to develop a solution to support the measurement activities throughout the process lifecycle. Method: Study the current state of the art to identify existing gaps. Then, propose a solution to support the process measurement throughout the SDP lifecycle. Results: The proposed solution consists of two main components: (i) Measurement lifecycle, which defines the measurement activities throughout the SDP lifecycle, (ii) Measurement definition metamodel (MDMM), which supports the measurement lifecycle and its integration into the process lifecycle. Conclusion: This proposal allows organizations to define, manage, and improve their processes; the proposed information model supports the unification of the measurement concepts and vocabulary. The defined measurement lifecycle provides a comprehensive guide for the organizations to establish the measurement objectives and carry out the necessary activities to achieve them. The proposed MDMM supports and guides the engineers in the complete and operational definition of the measurement concepts.

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“…In the first group, there are approaches such as BP-QL [1], BPMN-Q [22], APQL [23] that only query the business process viewpoint or IPMPQL [5], Bee-hiveZ [16], BP-Mon [2] and BP-SPARQL [3] only query the business process instance viewpoint. In the second group, is PIQL [19] that queries, on the one hand, the business process viewpoint, and, on the other hand, the combination of the business process instances and data viewpoints. Data-Aware POQL [7] also belongs to the second group by querying the combination of Business Process Instance and Business Data viewpoints.…”

Section: Related Workmentioning

confidence: 99%

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Gómez-López

Quintero

Parody

et al. 2018

Business Process Management Workshops

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Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three different technologies.

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Process Instance Query Language to Include Process Performance Indicators in DMN

Cited by 17 publications

References 16 publications

Decision-making support for input data in business processes according to former instances

Decision-making support for input data in business processes according to former instances

An MDA Proposal To Integrate the Measurement Lifecycle Into the Process Lifecycle

An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

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