A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

Conformance checking and performance improvement in scheduled processes: A queueing-network perspective

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today's economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking (how to assess the conformance of the schedule and the actual process execution) and performance improvement (how to improve the operational process performance). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%

Ensemble-based prediction of business processes bottlenecks with recurrent concept drifts

Bottleneck prediction is an important sub-task of process mining that aims at optimizing the discovered process models by avoiding such congestions. This paper discusses an ongoing work on incorporating recurrent concept drift in bottleneck prediction when applied to a real-world scenario. In the field of process mining, we develop a method of predicting whether and which bottlenecks will likely appear based on data known before a case starts. We next introduce GRAEC, a carefully-designed weighting mechanism to deal with concept drifts. The weighting decays over time and is extendable to adapt to seasonality in data. The methods are then applied to a simulation, and an invoicing process in the field of installation services in real-world settings. The results show an improvement to prediction accuracy compared to retraining a model on the most recent data.</p

User Experience Assessment of E-Learning System Using USE Questionaries

The rapid growth of e-learning has gained significant popularity as customized mode of education. The opportunity to rebuild the e-learning as a challenge to assess the usability degree of e-learning are open. This study focuses on measuring the evaluation of usability of interactive-coding web-based learning module that has been developed. The study involved 59 students from Industrial Engineering Study Program who are actively used the e-learning for algorithm and programming course. The methodology used in this research is descriptive correlative method with a quantitative approach by collecting the usability measurement using the USE Questionnaire, which consist of for indicators, namely usefulness, ease of use, ease of learning, and satisfaction) represented by 30 questions with four possible Likert scale options. The research finding indicates that all the participants are satisfied with the four assessment variables and 56% are have positive comments and 44% has improvement comments. In the multiple linear regression analysis showed for F-Test that the independent variables (Usefulness, Ease of Use, and Ease of Learning) simultaneously have a significant effect on the dependent variable (Satisfaction) and for Partial t-test showed that there is a positive and significant relationship between Usefulness and Ease of Learning on satisfaction variables, meanwhile, there is no positive and significant relationship between Ease of Use on satisfaction variables

A General Framework for Predictive Business Process Monitoring

Abstract. As organizations gain awareness of the potential business value locked in their process execution event logs, &quot;evidence-based&quot; business process management (BPM) becomes a common tool for process analysts. In contrast to traditional process monitoring techniques which are typically performed using data from running process instances only, predictive evidence-based BPM methods tap also into historical data, to allow process workers to respond, in real-time, to specific process performance issues and compliance violations as they arise or even before they arise. In previous work, various approaches have been proposed to address typical predictive process monitoring problems, such as whether a running process instance will meet its performance targets, or when will an instance be finally finished. However, these approaches are rather ad-hoc and lack generality, as they tackle only particular, pre-defined aspects of predictive monitoring and often only work with specific characteristics of the dataset. The proposed research project aims at developing a general and robust framework for predictive process monitoring that will address a variety of process monitoring tasks such as predicting the outcome of individual activities or of the whole process instance, or predicting the completion path of an instance

Sharing delay information in service systems: a literature survey

Service providers routinely share information about upcoming waiting times with their customers, through delay announcements. The need to effectively manage the provision of these announcements has led to a substantial growth in the body of literature which is devoted to that topic. In this survey paper, we systematically review the relevant literature, summarize some of its key ideas and findings, describe the main challenges that the different approaches to the problem entail, and formulate research directions that would be interesting to consider in future work