Discovering Business Process Simulation Models in the Presence of Multitasking

Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.European Research Council PIX 834141Junta de Andalucía P12--TIC--1867Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C2

Discovering Business Process Simulation Models in the Presence of Multitasking

Business process simulation is a versatile technique for analyzing business processes from a quantitative perspective. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, several authors have proposed to discover simulation models from process execution logs so that the resulting simulation models more closely match reality. Existing techniques in this field assume that each resource in the process performs one task at a time. In reality, however, resources may engage in multitasking behavior. Traditional simulation approaches do not handle multitasking. Instead, they rely on a resource allocation approach wherein a task instance is only assigned to a resource when the resource is free. This inability to handle multitasking leads to an overestimation of execution times. This paper proposes an approach to discover multitasking in business process execution logs and to generate a simulation model that takes into account the discovered multitasking behavior. The key idea is to adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original processing times. The proposed approach is evaluated using a real-life dataset and synthetic datasets with different levels of multitasking. The results show that, in the presence of multitasking, the approach improves the accuracy of simulation models discovered from execution logs.Comment: Accepted at The 14th International Conference on Research Challenges in Information Science (RCIS 2020). 17 pages, 4 figure

Discovering business process simulation models in the presence of multitasking and availability constraints

Business process simulation is a versatile technique for quantitative analysis of business processes. A well-known limitation of process simulation is that the accuracy of the simulation results is limited by the faithfulness of the process model and simulation parameters given as input to the simulator. To tackle this limitation, various authors have proposed to discover simulation models from process execution logs, so that the resulting simulation models more closely match reality. However, existing techniques in this field make certain assumptions about resource behavior that do not typically hold in practice, including: (i) that each resource performs one task at a time; and (ii) that resources are continuously available (24/7). In reality, resources may engage in multitasking behavior and they work only during certain periods of the day or the week. This article proposes an approach to discover process simulation models from execution logs in the presence of multitasking and availability constraints. To account for multitasking, we adjust the processing times of tasks in such a way that executing the multitasked tasks sequentially with the adjusted times is equivalent to executing them concurrently with the original times. Meanwhile, to account for availability constraints, we use an algorithm for discovering calendar expressions from collections of time-points to infer resource timetables from an execution log. We then adjust the parameters of this algorithm to maximize the similarity between the simulated log and the original one. We evaluate the approach using real-life and synthetic datasets. The results show that the approach improves the accuracy of simulation models discovered from execution logs both in the presence of multitasking and availability constraintsEuropean Research Council PIX 834141Ministerio de Ciencia, Innovación y Universidades OPHELIA RTI2018-101204-B-C22Junta de Andalucía EKIPMENTPLUS (P18–FR–2895

Aligning business processes and work practices

Current business process modeling methodologies offer little guidance regarding how to keep business process models aligned with their actual execution. This paper describes how to achieve this goal by uncovering and supervising business process models in connection with work practices using BAM. BAM is a methodology for business process modeling, supervision and improvement that works at two dimensions; the dimension of processes and the dimension of work practices. The business modeling component of BAM is illustrated with a case study in an organizational setting

Computer Multitasking in the Classroom: Training to Attend or Wander?

This study aimed to examine the phenomenon of Psy.D. students’ multitasking on the computer while in the classroom. Using an online survey of 45 questions, the study invited Psy.D. students from across the US to answer questions pertaining to their non-class-related use of computers in the classroom, including an exploration of their relationship with computers and the internet, feelings and judgments regarding multitasking in the classroom, and opinions on the behavior and its potential impact on their profession. A total of 166 people visited the survey with 145 respondents who answered it to completion. Of the 145 participants, 86% (125) were female, 10% (14) were male, and 3.5% (5) were non-binary. The mean age was 28.5, with ages ranging from 22 to 52 and over. Approximately 85% (124) of the respondents acknowledged multitasking on their computers or devices while in class. A significant negative relationship was found between whether or not students viewed this topic as a problem and how much time they spent multitasking in class. A significant positive relationship was found between the students’ age and their level of negative judgment of others who multitask. The overall amount of neutrality and positivity towards multitasking among students was greater than expected, which illuminated this topic as being much more complex than originally conceived. This raised further questions about the current academic context within which students are multitasking, with considerations for finding ways to adapt teaching methods that can respond to ongoing neurological shifts in a new generation of students

University Traveler Value of Potential Real-Time Transit Information

Intelligent transportation systems (ITS) have become common in public transit systems, particularly providing real-time transit information. For new implementations, it remains difficult to predict and quantify system and user benefits of technology implementation. Although previous studies have quantified the operational benefits of real-time transit traveler information systems, a gap in knowledge exists around passenger benefits of such systems. The objective of this research was to create a refined method for evaluating transit rider benefits from real-time traveler information and predict changes in traveler behavior. The study was conducted on a rural university campus, isolating the impacts of the system from the multiple influences that often affect transportation in larger metropolitan areas. This study uniquely integrated transit system performance, pedestrian travel times, and traffic simulation to determine travel times and predict mode split. Findings indicated that reducing passenger waiting anxiety was the most significant measure of traveler benefit from such a system. While the benefits found were specific to the study site, the methodology can be used for other transit systems evaluating real-time transit technology investments in rural or urban environments

The classroom as a research community: an innovative methodological approach for e-learning.

The crisis generated by the pandemic has also affected our education system and challenged traditional teaching/learning models both in school and university. Technology has changed learning and emphasized the collaborative and distributed dimensions of knowledge. Classrooms can be considered as knowledge communities, where active learning, the dimension of research, and comparison with peers represent the values of citizenship that is also digital citizenship. The methodological approach defined as "Bayes' class" fits into this context. Bayes' class is a theoretical-operational approach to learning based on research, experience, and discovery. In this paper we present the results of a teaching activity conducted during the lockdown. We started with these questions: how to use technologies to redesign a digitally enhanced learning environment for university internship activities in the look-down phase? How to redesign indirect internship activities? How to continue to accompany future teachers at a distance in building their knowledge, skills, attitudes, and sensibilities? The focus group, conducted at the end of the experience, confirmed that the “Bayes’ Class” teaching setting, promotes a participatory culture based on interaction, peering, and multitasking. In addition, the experience allowed for the enhancement of the indirect internship as a community of practic

Using agent-based modelling and simulation to model performance measurement in healthcare

One of the priority areas of the UK healthcare system is urgent and emergency care, especially accident and emergency departments (A&E departments). Currently, there is much interest in studying the unintended consequences of the current UK healthcare performance system. Simulation modelling has been proved to be a useful tool for modelling different aspects of the healthcare systems, particularly those related to the performance of A&E departments. Most of the available literature on modelling A&E departments focus on supporting operational decision-making and planning in specific healthcare units to study particular problems such as staff scheduling, resource utilisation, and waiting time issues. That is, most simulation studies focus on analysing how different configurations of healthcare systems affect their performance. However, to our knowledge, few simulation studies focus on explaining how human behaviour affects the performance of the system, and very few have studied how, in turn, performance targets set for A&E departments affect human behaviour in healthcare systems. Some aspects of human behaviour have been incorporated within existing simulation models, though with limitations. In fact, most studies have aimed to study patients’ behaviour, and few have included some aspects of the behaviour of clinical staff. Here we consider how to model clinician behaviour in relation to the performance of A&E departments. This thesis presents an exploratory study of the use of agent-based modelling and simulation (ABMS) and discrete event simulation (DES) to demonstrate how to model clinician behaviour within an A&E department and how that behaviour is related to waiting time performance. Clinical behaviour, incorporated in the simulation models developed here, employs a framework called PECS that assumes that behaviour is influenced by Physical (P), Emotional (E), Cognitive (C) and Social (S) factors. A discussion of the advantages and limitations of the use of ABMS and DES to model such behaviour is included. The findings of this research demonstrate that ABMS is well suited to simulate human behaviour in an A&E department. However, it is not explicitly designed to model processes of complex operational and queue-based systems such as accident and emergency departments. In addition, this research work also demonstrates that DES is an adequate tool for modelling A&E’s processes and patient flows, that can, in fact, incorporate different aspects of human behaviour. Furthermore, the process of modelling human behaviour in DES is complex because, though most DES software allows the representation of reactive behaviour, they make it difficult to model other types of human behaviour The main contributions of this thesis are: 1) a comparison and evaluation of how suitable ABMS and DES are for modelling clinical behaviour, 2) an approach to model the relationship between human behaviour and waiting time performance, considering four aspects of human behaviour (physical, emotional, cognitive and social)

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables

We investigate the challenging task of learning causal structure in the presence of latent variables, including locating latent variables and determining their quantity, and identifying causal relationships among both latent and observed variables. To address this, we propose a Generalized Independent Noise (GIN) condition for linear non-Gaussian acyclic causal models that incorporate latent variables, which establishes the independence between a linear combination of certain measured variables and some other measured variables. Specifically, for two observed random vectors $\bf{Y}$ and $\bf{Z}$, GIN holds if and only if $\omega^{\intercal}\mathbf{Y}$ and $\mathbf{Z}$ are independent, where $\omega$ is a non-zero parameter vector determined by the cross-covariance between $\mathbf{Y}$ and $\mathbf{Z}$. We then give necessary and sufficient graphical criteria of the GIN condition in linear non-Gaussian acyclic causal models. Roughly speaking, GIN implies the existence of an exogenous set $\mathcal{S}$ relative to the parent set of $\mathbf{Y}$ (w.r.t. the causal ordering), such that $\mathcal{S}$ d-separates $\mathbf{Y}$ from $\mathbf{Z}$. Interestingly, we find that the independent noise condition (i.e., if there is no confounder, causes are independent of the residual derived from regressing the effect on the causes) can be seen as a special case of GIN. With such a connection between GIN and latent causal structures, we further leverage the proposed GIN condition, together with a well-designed search procedure, to efficiently estimate Linear, Non-Gaussian Latent Hierarchical Models (LiNGLaHs), where latent confounders may also be causally related and may even follow a hierarchical structure. We show that the underlying causal structure of a LiNGLaH is identifiable in light of GIN conditions under mild assumptions. Experimental results show the effectiveness of the proposed approach

Memes, Args And Viral Videos: Spreadable Media, Participatory Culture, And Composition Pedagogy

This project argues that spreadable media texts motivate people to engage in compositional activities advocated in First Year Composition (FYC). Drawing on Henry Jenkins’ assertion that participatory culture offers potential for learning, I use his list of eleven participatory culture skills that he believed necessary for all students. After showing how well the Participatory Culture Abilities (PCAs) align with the WPA Outcomes Statement (WPA OS), I put forth the WPA OS and the PCAs combined as a lens through which to view three spreadable media case studies: Spreadable Media Events, Fan Labor, and Alternate Reality Games. Based on my findings, I conclude that we should incorporate Spreadable Media and Participatory Design pedagogy into the composition classroom, which will lead to innovative pedagogical practices that foster agency and engagement in students towards their writing. It will inform and facilitate the achievement of the Writing Program Administrators’ outcomes; and it will support the learning of a set of participatory culture abilities that will help students to become conscious, responsible and empowered users of their rhetorical power in digital environments