Enabling process mining on sensor data from smart products

Eck, Maikel L. van; Sidorova, Natalia; Aalst, Wil M. P. van der

doi:10.1109/rcis.2016.7549355

Cited by 41 publications

(26 citation statements)

References 19 publications

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“…In the current research, PM based on customer data focuses on process discovery. Van Eck et al [54] aim at discovering user behavior using sensor data gathered from a smart product. In their use case, they investigated the feeding process of a baby with a sensor-equipped bottle.…”

Section: Related Workmentioning

confidence: 99%

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Zilker¹,

Marx²,

Stierle³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experience.

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Section: Related Workmentioning

confidence: 99%

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Zilker¹,

Marx²,

Stierle³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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show abstract

“…In van Eck et al (2016), the authors translate continuous sensor data into higher-level events. The only input required is a minimal window size, in which the input sensor data needs to be segmented.…”

Section: Supervised Techniquesmentioning

confidence: 99%

Event abstraction in process mining: literature review and taxonomy

et al. 2020

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The execution of processes in companies generates traces of event data, stored in the underlying information system(s), capturing the actual execution of the process. Analyzing event data, i.e., the focus of process mining, yields a detailed understanding of the process, e.g., we are able to discover the control flow of the process and detect compliance and performance issues. Most process mining techniques assume that the event data are of the same and/or appropriate level of granularity. However, in practice, the data are extracted from different systems, e.g., systems for customer relationship management, Enterprise Resource Planning, etc., record the events at different granularity levels. Hence, pre-processing techniques that allow us to abstract event data into the right level of granularity are vital for the successful application of process mining. In this paper, we present a literature study, in which we assess the state-of-the-art in the application of such event abstraction techniques in the field of process mining. The survey is accompanied by a taxonomy of the existing approaches, which we exploit to highlight interesting novel directions.

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“…Initial approaches can be found that apply process mining to raw data. For instance, a transformation approach is discussed by van Eck et al [35] applying process mining techniques to sensor data. The available approaches have in common that they intend to map sensor data to activities beforehand.…”

Section: Existing Work Related To Q1mentioning

confidence: 99%

From event streams to process models and back: Challenges and opportunities

Soffer

Hinze

Koschmider

et al. 2019

Information Systems

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The domains of complex event processing (CEP) and business process management (BPM) have different origins but for many aspects draw on similar concepts. While specific combinations of BPM and CEP have attracted research attention, resulting in solutions to specific problems, we attempt to take a broad view at the opportunities and challenges involved. We first illustrate these by a detailed example from the logistics domain. We then propose a mapping of this area into four quadrants -two quadrants drawing from CEP to create or extend process models and two quadrants starting $ This paper is an outcome of discussions and collaborations that were initiated at the Dagstuhl seminar 16341 on "Integrating Process-Oriented and Event-Based Systems" from a process model to address how it can guide CEP. Existing literature is reviewed and specific challenges and opportunities are indicated for each of these quadrants. Based on this mapping, we identify challenges and opportunities that recur across quadrants and can be considered as the core issues of this combination. We suggest that addressing these issues in a generic manner would form a sound basis for future applications and advance this area significantly.

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Enabling process mining on sensor data from smart products

Cited by 41 publications

References 19 publications

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience

Event abstraction in process mining: literature review and taxonomy

From event streams to process models and back: Challenges and opportunities

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