Context-Aware Predictive Process Monitoring: The Impact of News Sentiment

Yeshchenko, Anton; Durier, Fernando; Revoredo, Kate; Mendling, Jan; Santoro, Flávia Maria

doi:10.1007/978-3-030-02610-3_33

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…Finally, the managerial and organizational implications revealed in this study may inform research on the technical aspects of process mining. For example, the integration of various contextual factors is important for decision-making (Grisold et al , 2020b; Yeshchenko et al , 2018).…”

Section: Discussionmentioning

confidence: 99%

“…In this area, there are numerous works that develop and improve algorithms and process mining techniques. These works aim to achieve more precise measures (Huang and Kumar, 2012; Polyvyanyy et al , 2018), detect different process dynamics (Bose et al , 2013), integrate social and organizational information (Song and van der Aalst, 2008) and account for contextual factors (Yeshchenko et al , 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…While some works in this discourse set out to illuminate the practical aspects of process mining (Turner et al , 2012; Thiede et al , 2018; Emamjome et al , 2019; van der Aalst, 2019; van der Aalst et al , 2007; De Weerdt et al , 2013), the majority of research is concerned with technical aspects, e.g. developing and improving process mining techniques (Augusto et al , 2018; Tax et al , 2019; Di Ciccio et al , 2017; Yeshchenko et al , 2018).…”

Section: Introductionmentioning

confidence: 99%

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Adoption, use and management of process mining in practice

Grisold

Mendling

Otto

2020

BPMJ

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PurposeThis study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.Design/methodology/approachWe report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.FindingsWe find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.Research limitations/implicationsWe point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.Practical implicationsThe reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.Originality/valueThis study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

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Section: Discussionmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adoption, use and management of process mining in practice

Grisold

Mendling

Otto

2020

BPMJ

Self Cite

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“…For the sake of the verifiability, all the information for the replication of experiments is available online. For each case study, the documentation for a better understanding of the logs, the interviews with the process experts, the list of selected attributes for each dataset and the code of CAP3 project can be found in our Github repository 10 The TM [40] and SHP [41] datasets can be found in Zenodo repository.…”

Section: Verifiabilitymentioning

confidence: 99%

Context-Aware Process Performance Indicator Prediction

et al. 2020

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“…It can be defined as the "minimum set of variables containing all relevant information that impact the design[, implementation] and execution of a business process" [23, p.154]. Context information, originating from sources external [24,25] or internal (e.g. [26,15]) to the business process, can improve process predictions since it adds valuable information to the predictive models [5].…”

Section: Introductionmentioning

confidence: 99%

An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

Weinzierl¹,

Zilker²,

Brunk³

et al. 2020

Preprint

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Researchers have proposed a variety of predictive business process monitoring (PBPM) techniques aiming to predict future process behaviour during the process execution. Especially, techniques for the next activity prediction anticipate great potential in improving operational business processes. To gain more accurate predictions, a plethora of these techniques rely on deep neural networks (DNNs) and consider information about the context, in which the process is running. However, an in-depth comparison of such techniques is missing in the PBPM literature, which prevents researchers and practitioners from selecting the best solution for a given event log. To remedy this problem, we empirically evaluate the predictive quality of three promising DNN architectures, combined with five proven encoding techniques and based on five context-enriched real-life event logs. We provide four findings that can support researchers and practitioners in designing novel PBPM techniques for predicting the next activities.

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Context-Aware Predictive Process Monitoring: The Impact of News Sentiment

Cited by 14 publications

References 26 publications

Adoption, use and management of process mining in practice

Adoption, use and management of process mining in practice

Context-Aware Process Performance Indicator Prediction

An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs

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