On the Contextualization of Event-Activity Mappings

Koschmider, Agnes; Mannhardt, Felix; Heuser, Tobias

doi:10.1007/978-3-030-11641-5_35

Cited by 20 publications

(13 citation statements)

References 43 publications

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“…Mapping low-level events to activities for process mining is still a challenge [17]. Leotta et al [18] envision to use similar techniques as we employ: however, only discuss challenges.…”

Section: Related Workmentioning

confidence: 99%

Process Model Discovery from Sensor Event Data

Janssen

Mannhardt

Koschmider

et al. 2021

Lecture Notes in Business Information Processing

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Virtually all techniques, developed in the area of process mining, assume the input event data to be discrete, and, at a relatively high level (i.e., close to the business-level). However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, e.g., sensor data. Hence, in this paper, we present a novel technique that allows us to translate sensor data into higher-level, discrete event data, thus enabling existing process mining techniques to work on data tracked at a sensory level. Our technique discretises the observed sensor data into activities by applying unsupervised learning in the form of clustering. Furthermore, we refine the observed sequences by deducing imperative sub-models for the observed discretised data, i.e., allowing us to identify concurrency and interleaving within the data. We evaluated the approach by comparing the obtained model quality for several clustering techniques on a publicly available data-set in a smart home scenario. Our results show that applying our framework combined with a clustering technique yields results on data that otherwise would not be suitable for process discovery.

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“…Mapping low-level events to activities for process mining is still a challenge [17]. Leotta et al [18] envision to use similar techniques as we employ: however, only discuss challenges.…”

Section: Related Workmentioning

confidence: 99%

Process Model Discovery from Sensor Event Data

Janssen

Mannhardt

Koschmider

et al. 2021

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

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“…Consider Fig. 4, in which we sketch this hierarchy (Koschmider et al 2018). Within the figure, the cloud represents observations in the physical world, translated to ''low-level events,'' i.e., fine-granular events in our context.…”

Section: Event Abstractionmentioning

confidence: 99%

“…Within this dimension, we identify two main categories, i.e., strictly sequential-and interleaved Fig. 4 Hierarchy of mappings (Koschmider et al 2018); from raw data captured in the physical world, to activity instances that can be correlated to process instances techniques. Strictly sequential techniques only allow us to map strict sub-sequences of fine-granular events to a coarse-granular event, e.g., as examplified in Fig.…”

Section: Fine-granular Event Interleavingmentioning

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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“…These patterns are derived from lowlevel events using domain knowledge and then used to build an abstracted event log by exploiting existing alignment techniques. In [25] Koschmider et al investigate the role of context-awareness in event-to-activity mappings and propose a framework to support the inclusion of context information in the event logs and the comparison of different mapping approaches. The authors also propose and discuss a modular framework for enabling process discovery from sensor data, explicitly considering the data aggregations needed for event correlation, activity discovery, event abstraction, and process discovery [26].…”

Section: Related Workmentioning

confidence: 99%

Towards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories

Seiger

Zerbato

Burattin

et al. 2020

2020 IEEE 24th International Enterprise Distributed Object Computing Workshop (EDOCW)

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The Internet of Things (IoT) enables software-based access to vast amounts of data streams from sensors measuring physical and virtual properties of smart devices and their surroundings. While sophisticated means for the control and data analysis of single IoT devices exist, a more process-oriented view of IoT systems is often missing. Such a lack of process awareness hinders the development of process-based systems on top of IoT environments and the application of process mining techniques for process analysis and optimization in IoT. We propose a framework for the stepwise correlation and composition of raw IoT sensor streams with events and activities on a process level based on Complex Event Processing (CEP). From this correlation we derive refined process event logs-possibly with ambiguities-that can be used for process analysis at runtime (i. e., online). We discuss the framework using examples from a smart factory.

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On the Contextualization of Event-Activity Mappings

Cited by 20 publications

References 43 publications

Process Model Discovery from Sensor Event Data

Process Model Discovery from Sensor Event Data

Event abstraction in process mining: literature review and taxonomy

Towards IoT-driven Process Event Log Generation for Conformance Checking in Smart Factories

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