Titas Savickas

Directed Acyclic Graph Extraction from Event Logs

The usage of probabilistic models in business process mining enables analysis of business processes in a more efficient manner. Although, the Bayesian belief network is one of the most common probabilistic models, possibilities to use it in business process mining are still not widely researched. Existing process mining approaches are incapable to extract directed acyclic graphs for representing Bayesian networks. This paper presents an approach for extraction of directed acyclic graph from event logs. The results obtained during the experiment show that the proposed approach is feasible and may be applied in practice.

Bayesian belief network application in process mining

Business process mining gains more and more attention from both scientific and business communities. Its value has been recognized in information systems reverse engineering field as it allows discover knowledge about the implementation and execution of business processes in order to analyse and improve them. The probabilistic model of business process execution is essential for its analysis, however, there is very little research done on this topic. In this paper, we present a novel approach on automatic extraction of probabilistic models from mined business processes. We present an example of application of our approach in a real world scenario. The results achieved by application of our approach can faciliate decision making for the business users and therefore may be used for process analysis or enhancement of information systems with additional decision support capabilities.

Belief network discovery from event logs for business process analysis

Abstract Business processes are a main part of any organization therefore it is essential to improve their execution. Analysis of real process data can provide useful insights. Process mining techniques can be applied to event logs containing data related to business process execution to discover business processes and their behaviour therefore improving decision support. This paper presents an approach to discover probabilistic belief network from event logs, which focuses on domain-specific data contained in the logs for the analysis of business process behaviour. For evaluation purposes, the approach is applied to predict the business process execution. Experiments presented in the paper showcase practical application of the approach for synthetic and real-life logs. Obtained results prove that the approach is suitable for follow-up activity prediction and the nature of the approach allows for it to be extended for other use cases, such as anomaly detection or business process simulation.

Shared Resource Model for Allocating Resources to Activities in BP Simulation

Resources shareability amongst simultaneous business process (BP) activities is one of the main issues in the BP simulation. Without realistic simulation of shared resources usage, it is difficult to obtain accurate BP performance measures. This paper presents an approach to model shareable resources and a set of rules to handle resources allocation and control their usage in the rule-based BP simulation. The presented model and set of constraints are formalized using the UML/OCL allowing validation of the proof of concept. The approach has been implemented in a prototype of BP simulation tool, and an example highlighting benefits of applying the approach in the BP simulation is presented.

An approach for allocation of shared resources in the rule-based business process simulation

Simulation is an important business process (BP) analysis technique that allows modelling various process execution scenarios under different circumstances. One of the key factors that influence process execution is the availability of resources required to accomplish process activities. Without realistic simulation of resources usage by diverse activities, it is difficult or even impossible to obtain proper simulated BP performance measures. In this paper, we tackle the problem of simulating resources allocation and usage among simultaneous BP activities. We present an approach to model shareable resources and a set of constraints to handle resources allocation and control their usage in the rule-based BP simulation. This approach has been implemented in a prototype of BP simulation tool, and we provide an example showing benefits of applying this approach in a BP simulation.