Automated discovery of business process simulation models from event logs

2020 | Decision Support Systems | Citations: 6

Authors: Camargo, Manuel; Dumas, Marlon; González-Rojas, Oscar

Abstract: Business process simulation is a versatile technique to estimate the performance ... Expand

Abstract: Business process simulation is a versatile technique to estimate the performance of a process under multiple scenarios. This, in turn, allows analysts to compare alternative options to improve a business process. A common roadblock for business process simulation is that constructing accurate simulation models is cumbersome and error-prone. Modern information systems store detailed execution logs of the business processes they support. Previous work has shown that these logs can be used to discover simulation models. However, existing methods for log-based discovery of simulation models do not seek to optimize the accuracy of the resulting models. Instead they leave it to the user to manually tune the simulation model to achieve the desired level of accuracy. This article presents an accuracy-optimized method to discover business process simulation models from execution logs. The method decomposes the problem into a series of steps with associated configuration parameters. A hyper-parameter optimization method is used to search through the space of possible configurations so as to maximize the similarity between the behavior of the simulation model and the behavior observed in the log. The method has been implemented as a tool and evaluated using logs from different domains. Collapse

Topics: business process management peer-to-peer model open source process mining source code

Methods: event log simulation business process modeling computational algorithm modeling language

An empirical study of Bayesian network parameter learning with monotonic influence constraints

2016 | Decision Support Systems | Citations: 0

Authors: Zhou, Yun; Fenton, Norman; Zhu, Cheng

Abstract: Learning the conditional probability table (CPT) parameters of Bayesian networks ... Expand

Abstract: Learning the conditional probability table (CPT) parameters of Bayesian networks (BNs) is a key challenge in real-world decision support applications, especially when there are limited data available. A conventional way to address this challenge is to introduce domain knowledge/expert judgments that are encoded as qualitative parameter constraints. In this paper we focus on a class of constraints which is naturally encoded in the edges of BNs with monotonic influences. Experimental results indicate that such monotonic influence constraints are widespread in practical BNs (all BNs used in the study contain such monotonic influences). To exploit expert knowledge about such constraints we have developed an improved constrained optimization algorithm, which achieves good parameter learning performance using these constraints, especially when data are limited. Specifically, this algorithm outperforms the previous state-of-the-art and is also robust to errors in labelling the monotonic influences. The method is applied to a real world medical decision support BN where we had access to expert-provided constraints and real hospital data. The results suggest that incorporating expert judgments about monotonic influence constraints can lead to more accurate BNs for decision support and risk analysis. Collapse

Topics: decision support earthquake Perl missing data

Methods: computational algorithm experiment simulation case study constraint programming

Cognitive Control Capabilities, Routinization Propensity, and Decision-Making Performance

2014 | Organization Science | Citations: 0

Authors: Laureiro-Martinez, Daniella

Abstract: This paper examines the cognitive and behavioral foundations of decision making ... Expand

Abstract: This paper examines the cognitive and behavioral foundations of decision making at the individual level. It is based on a study conducted with 86 graduate students and a model that combines the highly mindful cognitive control capabilities and the less-mindful routinization propensity to explain decision-making performance. The paper offers three contributions. First, I introduce and empirically observe cognitive control capabilities, i.e., the supervisory cognitive mechanisms through which individuals monitor and control their own attention processes. Second, I introduce and operationalize the concept of routinization propensity. This is an individual-difference variable capturing the tendency to develop and enact a behavioral repertoire of standard solutions. Third, I propose and test a model in which routinization propensity mediates the impact of cognitive control capabilities on decision-making performance. I show that both high and low levels of mindfulness are essential to maximize performance in strategic decision making. Counterintuitively, however, higher cognitive control capabilities are connected to higher levels of routinization propensity, which in turn enhance performance. These findings contribute to the development of an integrated theory of cognition, decision making, and learning. Collapse

Topics: decision making mindfulness personality innovation management missing data

Methods: descriptive statistic simulation survey neurophysiology data transformation

Theories: cognitive theory

Good Practice Guide for Managing IT Risk in Colombian Banking: Specification by Disciplines

2010 | Americas Conference on Information Systems | Citations: 0

Authors: Ayerbe, Camilo Méndez; Avendaño, Gustavo Camargo; Herrera, Andrea

Abstract: In this paper, we describe the work, findings, study case and contributions made ... Expand

Abstract: In this paper, we describe the work, findings, study case and contributions made in the development of the specification of the “Guía de buenas prácticas de gestión de riesgo de TI en el sector bancario colombiano”1. We present how doing the specification of the most important step of the guide, makes it a strong tool for managing IT risk in the Colombian banking sector. This was achieved by reviewing some of the most relevant theories in IT risk management, developing new models that exploit their best attributes, and presenting them from a business point of view. Finally, we present the results obtained from validating the new constructed models in our study case: the cheque clearing process of “Banco de la República”2; a core service in the organization that depends ninety percent on IT. Collapse

Topics: IT risk management risk management COBIT IT investment marketing management

Methods: cross sectional research ethnography qualitative interview

An Unorthodox Interpretive Approach in Information Systems Research: A Picture is Worth 1000 Words

2009 | International Conference on Information Systems | Citations: 0

Authors: Andrade, Antonio Diaz; Arthanari, Tiru

Abstract: In this paper we make the case of using photographs as a source of data for inte ... Expand

Abstract: In this paper we make the case of using photographs as a source of data for interpretive research in the information systems field, especially for ethnographic accounts. We treat photographs as knowledge objects and examine the richness of content and context they offer. Based on experiences from anthropology and sociology, we contend that photographs should be used along with other sources of data, not only during the analysis stage but also be included in the conventional written report. We analyze the researcher’s role along three moments of research: during the fieldwork as an image maker, during the analysis stage as an image analyzer and during the writing up as an image presenter. A study on the transmission of information and use of computers in rural communities in Peru is used to explain the use of photographs along these moments of research. Collapse

Topics: information system use anonymity communication service infrastructure social network marketing management

Methods: field study ethnography grounded theory qualitative interview data transformation