http://dl.gi.de/handle/20.500.12116/37931 2026-07-22T20:35:10Z 2026-07-22T20:35:10Z Demazure, Théophile Karran, Alexander Léger, Pierre-Majorique Labonté-LeMoyne, Élise Sénécal, Sylvain Fredette, Marc Babin, Gilbert http://dl.gi.de/handle/20.500.12116/37940 2022-01-17T12:19:07Z 2021-01-01T00:00:00Z

Enhancing Sustained Attention Demazure, Théophile; Karran, Alexander; Léger, Pierre-Majorique; Labonté-LeMoyne, Élise; Sénécal, Sylvain; Fredette, Marc; Babin, Gilbert Arguably, automation is fast transforming many enterprise business processes, transforming operational jobs into monitoring tasks. Consequently, the ability to sustain attention during extended periods of monitoring is becoming a critical skill. This manuscript presents a Brain-Computer Interface (BCI) prototype which seeks to combat decrements in sustained attention during monitoring tasks within an enterprise system. A brain-computer interface is a system which uses physiological signals output by the user as an input. The goal is to better understand human responses while performing tasks involving decision and monitoring cycles, finding ways to improve performance and decrease on-task error. Decision readiness and the ability to synthesize complex and abundant information in a brief period during critical events has never been more important. Closed-loop control and motivational control theory were synthesized to provide the basis from which a framework for a prototype was developed to demonstrate the feasibility and value of a BCI in critical enterprise activities. In this pilot study, the BCI was implemented and evaluated through laboratory experimentation using an ecologically valid task. The results show that the technological artifact allowed users to regulate sustained attention positively while performing the task. Levels of sustained attention were shown to be higher in the conditions assisted by the BCI. Furthermore, this increased cognitive response seems to be related to increased on-task action and a small reduction in on-task errors. The research concludes with a discussion of the future research directions and their application in the enterprise.

2021-01-01T00:00:00Z Sadiq, Mohd. Parveen, Azra Jain, S. K. http://dl.gi.de/handle/20.500.12116/37939 2022-01-17T12:19:07Z 2021-01-01T00:00:00Z

Software Requirements Selection with Incomplete Linguistic Preference Relations Sadiq, Mohd.; Parveen, Azra; Jain, S. K. Software requirements (SRs) selection is a multicriteria group decision making (MCGDM) problem whose objective is to select the SRs from the pool of the requirements on the basis of different criteria. In MCGDM, different decision makers have different opinions of the same requirement so it is difficult to decide which set of SRs to implement during the different releases of the software. During the MCGDM process, decision makers may use linguistic variables to specify preferences of requirements over other requirements. In real life applications, it has been observed that sometimes decision makers cannot evaluate the SRs due to their lack of knowledge and limited expertise related to the problem domain. In this situation, incomplete linguistic preference relations (LPRs) are constructed. In literature, SRs selection with incomplete LPRs is still an unresearched problem. Therefore, to address this issue, a method is presented for the selection of SRs with incomplete LPRs. Finally, the applicability of the proposed method is explained with the help of an example.

2021-01-01T00:00:00Z Aalst, Wil M. P. Hinz, Oliver Weinhardt, Christof http://dl.gi.de/handle/20.500.12116/37932 2022-01-17T12:19:07Z 2021-01-01T00:00:00Z

Resilient Digital Twins Aalst, Wil M. P.; Hinz, Oliver; Weinhardt, Christof

2021-01-01T00:00:00Z Poppe, Erik Pika, Anastasiia Wynn, Moe Thandar Eden, Rebekah Andrews, Robert Hofstede, Arthur H. M. http://dl.gi.de/handle/20.500.12116/37938 2022-01-17T12:19:07Z 2021-01-01T00:00:00Z

Extracting Best-Practice Using Mixed-Methods Poppe, Erik; Pika, Anastasiia; Wynn, Moe Thandar; Eden, Rebekah; Andrews, Robert; Hofstede, Arthur H. M. Problem Definition: Queensland’s Compulsory Third-Party (CTP) Insurance Scheme provides a mechanism for persons injured as a result of a motor vehicle accident to receive compensation. Managing CTP claims involves multiple stakeholders with potentially conflicting interests. It is therefore pertinent to investigate whether ‘best practice’ for claims processing can be identified and measured so all claimants receive fair and equitable treatment. The project set out to test the applicability of a mixed-method approach to identify ‘best-practice’ using qualitative, process mining, and data mining techniques in an insurance claims processing domain. Relevance: Existing approaches typically identify ‘best practice’ from literature or surveys of practitioners. The study provides insights into an alternative, mixed-method approach to deriving best practice from historical data and domain knowledge. Methodology: The study is a reflective analysis of insights gained from a practical application of a mixed-method approach to determine ‘best practice’. Results: The mixed-method approach has a number of benefits over traditional approaches in uncovering best practice process behavior from historical data in the real-world context (i.e., can identify process behavior differences between high and low performing cases). The study also highlights a number of challenges with regards to the quality and detail of data that needs to be available to perform the analysis. Managerial Implications: The ‘lessons learned’ from this study will directly benefit others seeking to implement a data-driven approach to understand a ‘best-practice’ process in their own organization.

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