Conformance Checking for Time-Series-Aware Processes

Rodríguez-Fernández, Víctor; Trzcionkowska, Agnieszka; González-Pardo, Antonio; Brzychczy, Edyta; Nalepa, Grzegorz J.; Camacho, David

doi:10.1109/tii.2020.2977126

Cited by 11 publications

(11 citation statements)

References 20 publications

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“…The framework proposed requires abstracting the IoT data to integrate them in an XES event log. A second work is proposed by Rodriguez-Fernandez et al [22], who present an approach for IoT-enhanced deviation detection in the time series data directly (in a so-called timeseries log). Remark that all these papers bumped into the limitations of traditional event logs and had to abstract the data first or to use the raw sensor data.…”

Section: Process Mining Using Iot Datamentioning

confidence: 99%

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NICE: The Native IoT-Centric Event Log Model for Process Mining

Bertrand,

Veneruso,

Leotta

et al. 2024

Lecture Notes in Business Information Processing

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More and more so-called IoT-enhanced business processes (BPs) are supported by IoT devices, which collect large amounts of data about the execution of such processes. While these data have the potential to reveal crucial insights into the execution of the BPs, the absence of a suitable event log format integrating IoT data to process data greatly hampers the realisation of this potential. In this paper, we present the Native Iot-Centric Event (NICE) log, a new event log format designed to incorporate IoT data into a process event log ensuring traceability, flexibility and limiting data loss. The new format was linked to a smart spaces data simulator to generate synthetic logs. We evaluate our format against requirements previously established for an IoTenhanced event log format, showing that it meets all requirements, contrarily to other alternative formats. We then perform an analysis of a synthetic log to show how IoT data can easily be used to explain anomalies in the process.

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Section: Process Mining Using Iot Datamentioning

confidence: 99%

“…In the last years, several data formats have been adapted to IoT-enhanced event logs [9,20,28,22]. In this section, we present these new alternatives and compare them to our data format with respect to the requirements identified in [5].…”

Section: Related Workmentioning

confidence: 99%

NICE: The Native IoT-Centric Event Log Model for Process Mining

Bertrand,

Veneruso,

Leotta

et al. 2024

Lecture Notes in Business Information Processing

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“…Other complementary XAI techniques based on visualizations will be considered with the aim to facilítate the work of medical staff. Finally, and to test the generality of the method proposed in this work, other domains, such as those related to industrial domains [85], where the combinations of CNN ensemble models and XAI methods, could increase the capabilities (analysis, prediction, explainability) of current used methods, will be explored and tested in the near future.…”

Section: Future Workmentioning

confidence: 99%

Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis

Liz

Sánchez-Montañés

Tagarro

et al. 2021

Future Generation Computer Systems

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“…This method aims at detecting concept drifts and does not quantify temporal deviations of a single instance when compared to a process model. [17] does not use a process execution log, but a data stream of all the data elements of a business process. These data elements are stored in time sequences and the behavior of these time sequences is evaluated.…”

Section: Related Workmentioning

confidence: 99%

Temporal Conformance Checking at Runtime based on Time-infused Process Models

Stertz,

Mangler,

Rinderle-Ma

2020

Preprint

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Conformance checking quantifies the deviations between a set of traces in a given process log and a set of possible traces defined by a process model. Current approaches mostly focus on added or missing events. Lately, multi-perspective mining has provided means to check for conformance with time and resource constraints encoded as data elements. This paper presents an approach for quantifying temporal deviations in conformance checking based on infusing the input process model with a temporal profile. The temporal profile is calculated based on an associated process log considering task durations and the temporal distance between events. Moreover, a simple semantic annotation on tasks in the process model signifies their importance with respect to time. During runtime, deviations between an event stream and the process model with the temporal profile are quantified through a cost function for temporal deviations. The evaluation of the approach shows that the results for two realworld data sets from the financial and a manufacturing domain hold the promise to improve runtime process monitoring and control capabilities.

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Conformance Checking for Time-Series-Aware Processes

Cited by 11 publications

References 20 publications

NICE: The Native IoT-Centric Event Log Model for Process Mining

NICE: The Native IoT-Centric Event Log Model for Process Mining

Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis

Temporal Conformance Checking at Runtime based on Time-infused Process Models

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