Niels Martin

The Use of Process Mining in Business Process Simulation Model Construction

The paper focuses on the use of process mining (PM) to support the construction of business process simulation (BPS) models. Given the useful BPS insights that are available in event logs, further research on this topic is required. To provide a solid basis for future work, this paper presents a structured overview of BPS modeling tasks and how PM can support them. As directly related research efforts are scarce, a multitude of research challenges are identified. In an effort to provide suggestions on how these challenges can be tackled, an analysis of PM literature shows that few PM algorithms are directly applicable in a BPS context. Consequently, the results presented in this paper can encourage and guide future research to fundamentally bridge the gap between PM and BPS.

The use of process mining in a business process simulation context: Overview and challenges

This paper focuses on the potential of process mining to support the construction of business process simulation (BPS) models. To date, research efforts are scarce and have a rather conceptual nature. Moreover, publications fail to explicit the complex internal structure of a simulation model. The current paper outlines the general structure of a BPS model. Building on these foundations, modeling tasks for the main components of a BPS model are identified. Moreover, the potential value of process mining and the state of the art in literature are discussed. Consequently, a multitude of promising research challenges are identified. In this sense, the current paper can guide future research on the use of process mining in a BPS context.

Retrieving batch organisation of work insights from event logs

Abstract Resources can organise their work in batches, i.e. perform activities on multiple cases simultaneously, concurrently or intentionally defer activity execution to handle multiple cases (quasi-) sequentially. As batching behaviour influences process performance, efforts to gain insight on this matter are valuable. In this respect, this paper uses event logs, data files containing process execution information, as an information source. More specifically, this work (i) identifies and formalises three batch processing types, (ii) presents a resource-activity centered approach to identify batching behaviour in an event log and (iii) introduces batch processing metrics to acquire knowledge on batch characteristics and its influence on process execution. These contributions are integrated in the Batch Organisation of Work Identification algorithm (BOWI), which is evaluated on both artificial and real-life data.

Retrieving Resource Availability Insights from Event Logs

Resources are a critical component of a business process as they execute the activities. These resources, especially human resources, are not permanently available and tend to be involved in multiple processes. However, a company might wish to analyze or model a single process. To this end, insights need to be gathered on the availability of a resource for a particular process. This paper presents a procedure to retrieve daily availability records from an event log, which express a resources availability for the process under analysis while taking into account (i) the temporal dimension of availability and (ii) intermediate availability interruptions. Both the daily availability records themselves and the resource availability metrics that are introduced allow managers and employees to gain understanding in resource allocation to a process. The outlined procedure and metrics are applied to a real-life call center log, showing the need to post-process the daily availability records. Post-processing increases their comprehensiveness and is required to obtain meaningful values for particular metrics.

Event log knowledge as a complementary simulation model construction input

Business process simulation models are typically built using model construction inputs such as documentation, interviews and observations. Due to issues with these information sources, efforts to further improve the realism of simulation models are valuable. Within this context, the present paper focuses on the use of process execution data in simulation model construction. More specifically, the behaviour of contemporary business processes is increasingly registered in event logs by process-aware information systems. Knowledge can be extracted from these log files using process mining techniques. This paper advocates the addition of event log knowledge as a model construction input, complementary to traditional information sources. A conceptual framework for simulation model construction is presented and the integration of event log knowledge during the modeling of particular simulation model building blocks is outlined. The use of event log knowledge is demonstrated in a simulation of the operations of a roadside assistance company.

Using Event Logs to Model Interarrival Times in Business Process Simulation

The construction of a business process simulation (BPS) model requires significant modeling efforts. This paper focuses on modeling the interarrival time (IAT) of entities, i.e. the time between the arrival of consecutive entities. Accurately modeling entity arrival is crucial as it influences process performance metrics such as the average waiting time. In this respect, the analysis of event logs can be useful. Given the limited process mining support for this BPS modeling task, the contribution of this paper is twofold. Firstly, an IAT input model taxonomy for process mining is introduced, describing event log use depending on process and event log characteristics. Secondly, ARPRA is introduced and operationalized for gamma distributed IATs. This novel approach to mine an IAT input model is the first to explicitly integrate the notion of queues. ARPRA is shown to significantly outperform a benchmark approach which ignores queue formation.

Affordances and limitations of learning analytics for computer-assisted language learning: a case study of the VITAL project

ABSTRACT Learning analytics (LA) has emerged as a field that offers promising new ways to prevent drop-out and aid retention. However, other research suggests that large datasets of learner activity can be used to understand online learning behaviour and improve pedagogy. While the use of LA in language learning has received little attention to date, available research suggests that LA could provide valuable insights into task design for instructors and materials designers, as well as help students with effective learning strategies and personalised learning pathways. This paper first discusses previous CALL research based on learner tracking and specific affordances of LA for CALL, as well as its inherent limitations and challenges. The second part of the paper analyses data arising from the VITAL project that implemented LA in different blended or distance learning settings. Statistical and process-mining techniques were applied to data from 285 undergraduate students on a Business French course. Results suggested that most students planned their self-study sessions in accordance with the flipped classroom design. Other metrics measuring active online engagement indicated significant differences between successful and non-successful students’ learner patterns. The research implied that valuable insights can be acquired through LA and the use of visualisation and process-mining tools.

Developing an emergency department crowding dashboard: A design science approach

BACKGROUND As an emergency department (ED) is a complex adaptive system, the analysis of continuously gathered data is valuable to gain insight in the real-time patient flow. To support the analysis and management of ED operations, relevant data should be provided in an intuitive way. AIM Within this context, this paper outlines the development of a dashboard which provides real-time information regarding ED crowding. METHODS The research project underlying this paper follows the principles of design science research, which involves the development and study of artifacts which aim to solve a generic problem. To determine the crowding indicators that are desired in the dashboard, a modified Delphi study is used. The dashboard is implemented using the open source Shinydashboard package in R. RESULTS A dashboard is developed containing the desired crowding indicators, together with general patient flow characteristics. It is demonstrated using a dataset of a Flemish ED and fulfills the requirements which are defined a priori. CONCLUSIONS The developed dashboard provides real-time information on ED crowding. This information enables ED staff to judge whether corrective actions are required in an effort to avoid the adverse effects of ED crowding.

Using process mining to model interarrival times: investigating the sensitivity of the arpra framework

Accurately modeling the interarrival times (IAT) is important when constructing a business process simulation model given its influence on process performance metrics such as the average flow time. To this end, the use of real data from information systems is highly relevant as it becomes more readily available. This paper considers event logs, a particular type of file containing process execution information, as a data source. To retrieve an IAT input model from event logs, the recently developed ARPRA framework is used, which is the first algorithm that explicitly integrates the notion of queues. This paper investigates ARPRAs sensitivity to the initial parameter set estimate and the size of the original event log. Experimental results show that (i) ARPRA is fairly robust for the specification of the initial parameter estimate and (ii) ARPRAs output represents reality more closely for larger event logs than for smaller logs.