Discovering Business Processes in CRM Systems by Leveraging Unstructured Text Data

Banziger, Rolf; Basukoski, Artie; Chaussalet, Thierry J.

doi:10.1109/hpcc/smartcity/dss.2018.00257

Cited by 21 publications

(11 citation statements)

References 13 publications

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“…Next, we performed a sophisticated analysis of the event log (which refers to the process mining engine functions in Figure 1). After execution of the Alpha algorithm in a RapidProM environment, which provides a Petri net and a marking [2,4,13,23,25], we discovered some new activity traces ( Figure 6). We can now update our first model with the obtained knowledge.…”

Section: Resultsmentioning

confidence: 99%

“…The still existing drawback of that direction is the complicated implementation, especially in systems where performance requirements are high. Similarly, the adaptation of latent Dirichlet allocation (LDA), proposed by the authors of Reference [23] as an unsupervised machine learning technique to automatically detect and assign labels to activities, encountered difficulties in an implementation, but it seems to be a promising direction since this framework does not require any human intervention.…”

Section: Related Workmentioning

confidence: 99%

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Process Discovery in Business Process Management Optimization

2019

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Appropriate business processes management (BPM) within an organization can help attain organizational goals. It is particularly important to effectively manage the lifecycle of these processes for organizational effectiveness in improving ever-growing performance and competitivity-building across the company. This paper presents a process discovery and how we can use it in a broader framework supporting self-organization in BPM. Process discovery is intrinsically associated with the process lifecycle. We have made a pre-evaluation of the usefulness of our facts using a generated log file. We also compared visualizations of the outcomes of our approach with different cases and showed performance characteristics of the cash loan sales process.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Process Discovery in Business Process Management Optimization

2019

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“…Banziger et al [6] have investigated the use of unsupervised machine learning to detect and assign labels to activities automatically. The unsupervised machine learning algorithm had been tested over the data from a CRM (Customer Relationship Management) System.…”

Section: Related Workmentioning

confidence: 99%

Discovering Business Processes from Email Logs Using fastText and Process Mining

Rashnavadi¹,

Behzadifard²,

Farzadnia³

et al. 2020

Preprint

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Communication has never been more accessible than today. With the help of Instant messengers and Email Services, millions of people can transfer information with ease, and this trend has affected organizations as well. There are billions of organizational emails sent or received daily, and their main goal is to facilitate the daily operation of organizations. Behind this vast corpus of human-generated content, there is much implicit information that can be mined and used to improve or optimize the organizations’ operations. Business processes are one of those implicit knowledge areas that can be discovered from Email logs of an Organization, as most of the communications are followed inside Emails. The purpose of this research is to propose an approach to discover the process models in the Email log. In this approach, we combine two tools, supervised machine learning and process mining. With the help of supervised machine learning, fastText classifier, we classify the body text of emails to the activity-related. Then the generated log will be mined with process mining techniques to find process models. We illustrate the approach with a case study company from the oil and gas sector.

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“…It usually involves analyzing process event logs and discovering the process models behind them (de Murillas, 2019). Several approaches can be used to discover them (Banziger, Basukoski, & Chaussalet, 2019;Calvanese, Kalayci, Montali, & Santoso, 2017;Van Der Aalst, Weijters, & Maruster, 2004).…”

Section: Introductionmentioning

confidence: 99%