Academic literature on the topic 'Business Process Monitoring'

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Journal articles on the topic "Business Process Monitoring"

1

Santoso, Ario, and Michael Felderer. "Specification-driven predictive business process monitoring." Software and Systems Modeling 19, no. 6 (2019): 1307–43. http://dx.doi.org/10.1007/s10270-019-00761-w.

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Abstract Predictive analysis in business process monitoring aims at forecasting the future information of a running business process. The prediction is typically made based on the model extracted from historical process execution logs (event logs). In practice, different business domains might require different kinds of predictions. Hence, it is important to have a means for properly specifying the desired prediction tasks, and a mechanism to deal with these various prediction tasks. Although there have been many studies in this area, they mostly focus on a specific prediction task. This work
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2

Kien Sia, Siew, and Boon Siong Neo. "Business process reengineering, empowerment and work monitoring." Business Process Management Journal 14, no. 5 (2008): 609–28. http://dx.doi.org/10.1108/14637150810903020.

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3

Spree, Florian. "Business Process Models in the Context of Predictive Process Monitoring." Revista de Informática Teórica e Aplicada 28, no. 1 (2021): 39–46. http://dx.doi.org/10.22456/2175-2745.106828.

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Predictive process monitoring is a subject of growing interest in academic research. As a result, an increased number of papers on this topic have been published. Due to the high complexity in this research area a wide range of different experimental setups and methods have been applied which makes it very difficult to reliably compare research results. This paper's objective is to investigate how business process models and their characteristics are used during experimental setups and how they can contribute to academic research. First, a literature review is conducted to analyze and discuss
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4

Sucharittham, Nanthawadee, Choochart Haruechaiyasak, Hieu Chi Dam, and Thanaruk Theeramunkong. "Multidimensional Sentiment Cube Mining for Process Monitoring." Trends in Sciences 19, no. 9 (2022): 3682. http://dx.doi.org/10.48048/tis.2022.3682.

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Process monitoring is essential for quality improvement because it is necessary to find the answers to which business issues need to be understood. In the era of social media, many critiques concern the business domain, including life insurance, which is one of the significant business sectors in Thailand. To utilize this useful cloud corpus for the business improvement process, we propose a novel methodology for process monitoring using the concept of multidimensional sentiment cube (MDSC) mining to raise usefulness with the business process model notation (BPMN). As the ability of MDC raise
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5

Oksanych, I., I. Shevchenko, O. Kaliuzhnyi, P. Savushkin, and V. Mizin. "ARCHITECTURE OF BUSINESS PROCESS MONITORING AND DISPATCHING SYSTEM." Transactions of Kremenchuk Mykhailo Ostrohradskyi National University 3 (June 23, 2020): 106–13. http://dx.doi.org/10.30929/1995-0519.2020.3.106-113.

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6

Metzger, Andreas, Philipp Leitner, Dragan Ivanovic, et al. "Comparing and Combining Predictive Business Process Monitoring Techniques." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 2 (2015): 276–90. http://dx.doi.org/10.1109/tsmc.2014.2347265.

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7

Kang, Dongwoo, Sunjae Lee, Kwangsoo Kim, and Jae Yeol Lee. "An OWL-based semantic business process monitoring framework." Expert Systems with Applications 36, no. 4 (2009): 7576–80. http://dx.doi.org/10.1016/j.eswa.2008.09.027.

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8

Knuplesch, David, Manfred Reichert, and Akhil Kumar. "A framework for visually monitoring business process compliance." Information Systems 64 (March 2017): 381–409. http://dx.doi.org/10.1016/j.is.2016.10.006.

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9

Przemyslaw, Dadel, Balawajder Mariusz, Radziszowski Dominik, and Zielinski Krzysztof. "Adaptive SOA Stack-Based Business Process Monitoring Platform." Computer Science 15, no. 2 (2014): 173. http://dx.doi.org/10.7494/csci.2014.15.2.173.

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10

Fardbastani, Mohammad Ali, Farshad Allahdadi, and Mohsen Sharifi. "Business process monitoring via decentralized complex event processing." Enterprise Information Systems 12, no. 10 (2018): 1257–84. http://dx.doi.org/10.1080/17517575.2018.1522453.

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