Resultados de búsqueda para Business Process Management

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phaseguides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.This paper was published in IEEE Access, 2020, Vol. 8, pp. 222050 – 222063, doi: 10.1109/ACCESS.2020.3044670

Organizations execute daily activities to meet their objectives. The performance of these activities can be fundamental for achieving a business objective, but they also imply the assumption of certain security risks that might go against a company’s security policies. A risk may be defined as the effects of uncertainty on the achievement of the goals of a company, some of which can be associated with security aspects (e.g., data corruption or data leakage). The execution of the activities can be choreographed using business processes models, in which the risk of the entire business process model derives from a combination of the single activity risks (executed in an isolated manner). In this paper, the problem of automatic security risk management in the current BPMS is addresses. First, a formalization of the risk elements according to process models is included. These elements are supported as a BPMN 2.0 extension of risk information that is analyzed to determine nonconformance regarding risk goals. In addition, a diagnosis of the risk associated with the activity responsible for the nonconformance is also carried out. To this end, the proposal applies mechanisms based on the model-based diagnosis in which activities are in nonconformance with regard to the acceptable level of risk. The automation of diagnosis is carried out using artificial intelligence techniques based on constraint programming. The proposal is supported by the implementation of a plug-in that enables the graphical specification of the extension and the automation of the verification and diagnosis process. To the best of our knowledge, this is the first published work that addresses the risk-aware design of business processes with automatic techniques.

Summary of the contribution

Predictive monitoring of business processes is a challenging topic of process min- ing which is concerned with the prediction of process indicators of running pro- cess instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for pre- dictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process man- agement systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).

Resource Assignment Language (RAL) is a language for the selection of organisational resources that can be used, for example, for the assignment of human resources to business process activities. Its formal semantics have allowed the automation of analysis operations in several phases of the business process lifecycle. RAL was designed considering a specific organisational metamodel and pursuing specific purposes. However, it can be extended to deal with similar problems in different domains and under different circumstances. In this paper, a methodology to extend RAL is introduced, and an extension to support another organisational metamodel is described as a proof-of-concept.

Performance calculation is a key factor to match corporate goals between different partners in process execution. However, although, a number of standards protocols and languages have recently emerged to support business process services in the industry, there is no standard related to monitoring of performance indicators over processes in these systems. As a consequence, BPMS use propietary languages to define measures and calculate them over process execution. In this paper, we describe two different approaches to compute performance mea- sures on business process decoupled from specific Business Process Man- agement System (BPMS) with an existing BPMS-independent language (PPINOT) to define indicators over business processes. Finally, some optimization techniques are described to increase calculation performance based on computing aggregated measures incrementally.