Predictive business operations management

Castellanos, Malú; Salazar, Norman; Casati, Fabio; Dayal, Umeshwar; Shan, Ming-Chien

doi:10.1504/ijcse.2006.014772

Cited by 26 publications

(19 citation statements)

References 4 publications

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“…In the next section we present the primary studies in detail, and classify them using a taxonomy. [25] Lakshmanan et al [21] Conforti et al [5,6] di Francescomarino et al [10] Leontjeva et al [22] van der Spoel et al [43] Verenich et al [45] Castellanos et al [4] Schwegmann et al [36,37], Gha as et al [18]…”

Section: Primary and Subsumed Studiesmentioning

confidence: 99%

“…In such an approach, the input from a domain expert is needed. e resulting buckets can, for instance, refer to context categories [18] or execution stages [4,36]. e aim of this survey and benchmark is to derive general principles by comparing methods that are applicable in arbitrary outcome-based predictive process monitoring scenarios and, thus, the methods that are based on domain knowledge about a particular dataset are le out of scope.…”

Section: Prefix Lengthmentioning

confidence: 99%

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Outcome-Oriented Predictive Process Monitoring

Teinemaa

Dumas

Rosa

et al. 2019

ACM Trans. Knowl. Discov. Data

263

281

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Predictive business process monitoring refers to the act of making predictions about the future state of ongoing cases of a business process, based on their incomplete execution traces and logs of historical (completed) traces. Motivated by the increasingly pervasive availability of ne-grained event data about business process executions, the problem of predictive process monitoring has received substantial a ention in the past years. In particular, a considerable number of methods have been put forward to address the problem of outcomeoriented predictive process monitoring, which refers to classifying each ongoing case of a process according to a given set of possible categorical outcomes -e.g., Will the customer complain or not? Will an order be delivered, canceled or withdrawn? Unfortunately, di erent authors have used di erent datasets, experimental se ings, evaluation measures and baselines to assess their proposals, resulting in poor comparability and an unclear picture of the relative merits and applicability of di erent methods. To address this gap, this article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process monitoring tasks based on nine real-life event logs.

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Section: Primary and Subsumed Studiesmentioning

confidence: 99%

Section: Prefix Lengthmentioning

confidence: 99%

Outcome-Oriented Predictive Process Monitoring

Teinemaa

Dumas

Rosa

et al. 2019

ACM Trans. Knowl. Discov. Data

263

281

View full text Add to dashboard Cite

show abstract

“…As a subset of such online data-driven approaches, predictive process monitoring exploits data related to past and current instances to predict the behavior, performance, and outcome of currently running instances (Breuker et al 2016;Maggi et al 2014;Conforti et al 2016). The predictive process monitoring approaches proposed over the last years can be classified according to the underlying prediction task (Marquez-Chamorro et al 2018): performance predictions such as the remaining cycle time of running instances (van der Aalst et al 2011b;Polato et al 2014;Rogge-Solti and Weske 2015;van Dongen et al 2008), predictions regarding partial or final outcomes of process execution (Castellanos et al 2005;Conforti et al 2013;Kang et al 2012), business rule violations (Metzger et al 2015;Maggi et al 2014;Leontjeva et al 2015;Di Francescomarino et al 2016), and predictions of the next event(s) (Evermann et al 2016;Breuker et al 2016;Lakshmanan et al 2013;Ceci et al 2014;Mehdiyev et al 2017;Schönig et al 2018), maybe including further information such as the performing resource (Evermann et al 2017b).…”

Section: Data-driven Approaches In Business Process Managementmentioning

confidence: 99%

Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

et al. 2020

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Predictive process monitoring aims at forecasting the behavior, performance, and outcomes of business processes at runtime. It helps identify problems before they occur and re-allocate resources before they are wasted. Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring. This circumstance reflects a lack of understanding about which event log properties facilitate the use of DL techniques. To address this gap, the authors compared the performance of DL (i.e., simple feedforward deep neural networks and long short term memory networks) and ML techniques (i.e., random forests and support vector machines) based on five publicly available event logs. It could be observed that DL generally outperforms classical ML techniques. Moreover, three specific propositions could be inferred from further observations: First, the outperformance of DL techniques is particularly strong for logs with a high variant-to-instance ratio (i.e., many non-standard cases).Second, DL techniques perform more stably in case of imbalanced target variables, especially for logs with a high event-to-activity ratio (i.e., many loops in the control flow). Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks. Due to the purposive sampling of event logs and techniques, these findings also hold for logs outside this study.

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“…Alarms are provided for early notification of probable abnormal terminations. In [3], Castellanos et al present a business operations management platform equipped with time series forecasting functionalities. This platform allows for predictions of metric values on running process instances as well as for predictions of aggregated metric values of future instances (e.g., the number of orders that will be placed next Monday).…”

Section: Related Workmentioning

confidence: 99%