IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Vitiello, Autilia; Stefano, Bruno Di; Aalst, Wil M. P. van der; Günther, C.; Verbeek, Eric

doi:10.1109/mci.2017.2670420

Cited by 48 publications

(23 citation statements)

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“…In this phase, HIS records are extracted from different sources (databases, repositories, etc.) and transformed to a standard event log format that is readable by process mining tools like CSV, XLS, XES [23] (XES was approved as the IEEE standard in 2017), among others. To perform this transformation, Disco (Fluxicon) () is a fast and reliable tool that transforms historical records to a low level event log.…”

Section: Methodsmentioning

confidence: 99%

Performance Analysis of Emergency Room Episodes Through Process Mining

Rojas

Burattin

Muñoz-Gama

et al. 2019

IJERPH

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The performance analysis of Emergency Room episodes is aimed at providing decision makers with knowledge that allows them to decrease waiting times, reduce patient congestion, and improve the quality of care provided. In this case study, Process Mining is used to determine which activities, sub-processes, interactions, and characteristics of episodes explain why some episodes have a longer duration. The employed method and the results obtained are described in detail to serve as a guide for future performance analysis in this domain. It was discovered that the main cause of the increment in the episode duration is the occurrence of a loop between the Examination and Treatment sub-processes. It was also found out that as the episode severity increases, the number of repetitions of the Examination–Treatment loop increases as well. Moreover, the episodes in which this loop is more common are those that lead to Hospitalization as discharge destination. These findings might help to reduce the occurrence of this loop, in turn lowering the episode duration and, consequently, providing faster attention to more patients.

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Section: Methodsmentioning

confidence: 99%

Performance Analysis of Emergency Room Episodes Through Process Mining

Rojas

Burattin

Muñoz-Gama

et al. 2019

IJERPH

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“…Data items collected from multiple information and technical systems can be of different types and conforming to multiple schemata, such as the XES IEEE standard [1]. In most cases, such data cannot be passed directly into automata to accomplish data sequence learning and classification.…”

Section: The Evolving Tokenized Transducer Model a Hybrid Classmentioning

confidence: 99%

“…The result of the pre-processing is then written on the consolidated data stream D c . The consolidated data stream at step k, denoted as D [k] c , comprises ordered k data items that are the result of the pre-processing procedures, which can be defined as D [k] c = {d [1] c , . .…”

Section: The Evolving Tokenized Transducer Model a Hybrid Classmentioning

confidence: 99%

“…These algorithms must have lower complexity than their batch counterparts. Data streams comprising data items made according to the XES IEEE standard [1] are considered for event streams and are specifically used in the process discovery context. In this article, we focus on methods for generic learning and classification of data sequences in data streams.…”

Section: Introductionmentioning

confidence: 99%

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Latent Process Discovery Using Evolving Tokenized Transducer

2019

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Today organizations capture and store an abundant amount of data from their interaction with clients, internal information systems, technical systems and sensors. Data captured this way comprises many useful insights that can be discovered by various analytical procedures and methods. Discovering regular and irregular data sequences in the captured data can reveal processes performed by the organization, which can be then assessed, measured and optimized, to achieve overall better performance, lower costs, resolve congestions, find potentially fraudulent activities and similar. Besides process discovery, capturing data sequences can give additional behavioral and tendency insights for various observations in the organization, such as sales dynamic, customer behaviour and similar. The issue is that most of the captured data intertwine multiple processes, customers, cases, products in a single data log or data stream. In this article, we propose an evolving tokenized transducer (ETT), capable of learning data sequences from a multi-contextual data log or stream. The proposed ETT is a semi-supervised relational learning method that can be used as a classifier on an unknown data log or stream, revealing previously learned data sequences. The proposed ETT was tested on multiple synthetic and real-life cases and datasets, such as dataset of retail sales sequences, hospital process log involving septic patient treatment and BPI challenge 2019 dataset. Test results are successful, revealing ETT as a prominent process discovery method.

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“…Among other things, the MDE community has developed solutions for the creation and evolution of modeling artifacts, support for collaborative development of large distributed teams, agile development, management of models linked to runtime systems, integration of heterogeneous models, etc. MDE is now considered a mature engineering discipline with a huge body of knowledge [13] that provides a broad range of modeling languages (e.g., UML [31], SysML [18], BPMN [3], Modelica [19], BIM [16], XES [1]), tools and methodologies addressing different development aspects.…”

Section: Introductionmentioning

confidence: 99%

Towards Model-Driven Digital Twin Engineering: Current Opportunities and Future Challenges

Bordeleau

Combemale

Eramo

et al. 2020

Communications in Computer and Information Science

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Digital Twins have emerged since the beginning of this millennium to better support the management of systems based on (real-time) data collected in different parts of the operating systems. Digital Twins have been successfully used in many application domains, and thus, are considered as an important aspect of Model-Based Systems Engineering (MBSE). However, their development, maintenance, and evolution still face major challenges, in particular: (i) the management of heterogeneous models from different disciplines, (ii) the bidirectional synchronization of digital twins and the actual systems, and (iii) the support for collaborative development throughout the complete life-cycle. In the last decades, the Model-Driven Engineering (MDE) community has investigated these challenges in the context of software systems. Now the question arises, which results may be applicable for digital twin engineering as well. In this paper, we identify various MDE techniques and technologies which may contribute to tackle the three mentioned digital twin challenges as well as outline a set of open MDE research challenges that need to be addressed in order to move towards a digital twin engineering discipline.

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IEEE 1849: The XES Standard: The Second IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

Cited by 48 publications

References 0 publications

Performance Analysis of Emergency Room Episodes Through Process Mining

Performance Analysis of Emergency Room Episodes Through Process Mining

Latent Process Discovery Using Evolving Tokenized Transducer

Towards Model-Driven Digital Twin Engineering: Current Opportunities and Future Challenges

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