IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams

Aalst, Wil M. P. van der; Günther, Christian; Bose, Joy; Vargas, Josep Carmona; Dumas, Marlon; Geffen, Frank van; Goel, Sukriti; Guzzo, Antonella; Khalaf, Rania; Kuhn, Rudolf B.; Lehto, Teemu; Mannhardt, Felix

doi:10.1109/ieeestd.2016.7740858

Cited by 54 publications

(36 citation statements)

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“…Finally, the data tier provides for persistent storage of user registration details, the storage of experiment meta-data in addition to unstructured and semi-structured files relating to the experiment, such as protocol documents and data files. Regarding the data files, the Open Data Initiative proposes the eXtensible Event Stream (XES) standard [3] as the preferred format for event data files, enabling a richer description of datasets through the attribution of semantics to data file (log file) content ( Figure 3).…”

Section: Open Data Initiative-the Platformmentioning

confidence: 99%

“…The ODI framework includes a common protocol for data collection, a common format for data exchange, a data repository and related tools to underpin research within the domain of activity recognition. As described in previous work [2], the ODI aspires to use the eXtensible Event Stream (XES) standard for event data storage [3] and an ontological model for the description of experiment metadata.…”

Section: Introductionmentioning

confidence: 99%

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Portal Design for the Open Data Initiative: A Preliminary Study

et al. 2018

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The Open Data Initiative (ODI) has been previously proposed to facilitate the sharing of annotated datasets within the pervasive health care research community. This paper outlines the requirements for the ODI portal based on the ontological data model of the ODI and its typical usage scenarios. In the context of an action research framework, the paper outlines the ODI platform, the design of a prototype user interface for the purposes of initial evaluation and its technical review by third-party researchers (n = 3). The main findings from the technical review were found to be the need for a more flexible user interface to reflect the different experimental configurations in the research community, provision for describing dataset usage, and dissemination conditions. The technical review also identified the value of permitting datasets with variable quality, as noisy datasets are useful in the testing of activity recognition algorithms. Revisions to the ODI ontology and platform are proposed based on the findings from this study.

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Section: Open Data Initiative-the Platformmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Portal Design for the Open Data Initiative: A Preliminary Study

et al. 2018

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“…This library provides an interface to insert data in an instantiation of this meta model and to access it in a similar way to how XES Logs are managed by the IEEE XES Standard [7]. However, under the hood it relies on SQL technology.…”

Section: Methodsmentioning

confidence: 99%

“…Efforts in this field have already been made as can be observed in [7] with the IEEE XES standard. This standard defines a structure to manage and manipulate logs, containing events and traces and the corresponding attributes.…”

Section: Meta Modelmentioning

confidence: 99%

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Connecting databases with process mining: a meta model and toolset

2018

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Process mining techniques require event logs which, in many cases, are obtained from databases. Obtaining these event logs is not a trivial task and requires substantial domain knowledge. In addition, an extracted event log provides only a single view on the database. To change our view, e.g., to focus on another business process and generate another event log, it is necessary to go back to the source of data. This paper proposes a meta model to integrate both process and data perspectives, relating one to the other. It can be used to generate different views from the database at any moment in a highly flexible way. This approach decouples the data extraction from the application of analysis techniques, enabling the application of process mining in different contexts.

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Process mining applications in the healthcare domain: A comprehensive review

Guzzo

Rullo

Vocaturo

2021

WIREs Data Min & Knowl

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Process mining (PM) is a well-known research area that includes techniques, methodologies, and tools for analyzing processes in a variety of application domains. In the case of healthcare, processes are characterized by high variability in terms of activities, duration, and involved resources (e.g., physicians, nurses, administrators, machineries, etc.). Besides, the multitude of diseases that the patients housed in healthcare facilities suffer from makes medical contexts highly heterogeneous. As a result, understanding and analyzing healthcare processes are certainly not trivial tasks, and administrators and doctors look for tools and methods that can concretely support them in improving the healthcare services they are involved in. In this context, PM has been increasingly used for a wide range of applications as reported in some recent reviews. However, these reviews mainly focus on discussion on applications related to the clinical pathways, while a systematic review of all possible applications is absent. In this article, we selected 172 papers published in the last 10 years, that present applications of PM in the healthcare domain. The objective of this study is to help and guide researchers interested in the medical field to understand the main PM applications in the healthcare, but also to suggest new ways to develop promising and not yet fully investigated applications. Moreover, our study could be of interest for practitioners who are considering applications of PM, who can identify and choose PM algorithms, techniques, tools, methodologies, and approaches, toward what have been the experiences of success.

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IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams

Cited by 54 publications

References 0 publications

Portal Design for the Open Data Initiative: A Preliminary Study

Portal Design for the Open Data Initiative: A Preliminary Study

Connecting databases with process mining: a meta model and toolset

Process mining applications in the healthcare domain: A comprehensive review

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