Process Mining Manifesto

Aalst, Wil M. P. van der; Adriansyah, A Arya; Medeiros, Ana Karla Alves de; Arcieri, Franco; Baier, Thomas; Blickle, Tobias; Bose, Jagadeesh Chandra; Brand, Peter van den; Brandtjen, Ronald; Buijs, Jcam Joos; Burattin, Andrea; Carmona, Josep; Castellanos, Malú; Claes, Jan; Cook, Jonathan E.; Costantini, Nicola; Curbera, Francisco; Damiani, Ernesto; Leoni, Massimiliano de; Delias, Pavlos; Dongen, Boudewijn F. van; Dumas, Marlon; Dustdar, Schahram; Fahland, Dirk; Ferreira, Diogo R.; Gaaloul, Walid; Geffen, Frank van; Goel, Sukriti; Günther, Christian; Guzzo, Antonella; Harmon, Paul; Hofstede, Arthur H. M. ter; Hoogland, John L.; Ingvaldsen, Jon Espen; Kato, K.; Kuhn, Rudolf B.; Kumar, Akhil; Rosa, Marcello La; Maggi, Fabrizio Maria; Malerba, Donato; Mans, Ronny S.; Manuel, Alberto; McCreesh, Martin; Mello, Paola; Mendling, Jan; Montali, Marco; Motahari-Nezhad, Hamid Reza; Muehlen, Michael zur; Muñoz-Gama, Jorge; Pontieri, Luigi; Ribeiro, Joel; Rozinat, A Anne; Pérez, Hugo Seguel; Pérez, Ricardo Seguel; Sepúlveda, Marcos; Sinur, Jim; Soffer, Pnina; Song, Minseok; Sperduti, Alessandro; Stilo, Giovanni; Stoel, Casper; Swenson, Keith D.; Talamo, Maurizio; Tan, Wei; Turner, Christopher; Vanthienen, Jan; Varvaressos, George; Verbeek, Eric; Verdonk, Marc; Vigo, Roberto; Wang, Jianmin; Weber, Barbara; Weidlich, Matthias; Weijters, Ton; Wen, Lijie; Westergaard, Michael; Wynn, Moe Thandar

doi:10.1007/978-3-642-28108-2_19

Cited by 747 publications

(502 citation statements)

References 2 publications

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“…The guiding principles and challenges were defined by an IEEE Task Force on Process Mining in the Process Mining Manifesto (2011) which aimed to "increase the maturity of process mining as a new tool to improve the (re)design, control and support of operational business processes" [13] (page 1). There are a range of process mining algorithms but most have problems when analysing event data from clinical workflows, either in failure to construct useful process models or in models which do not reflect reality, mainly because of incomplete and noisy input data [12].…”

Section: Process Mining In Oncologymentioning

confidence: 99%

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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Abstract-Process mining, an emerging data analytics method, has been used effectively in various healthcare contexts including oncology, the study of cancer. Cancer is a complex disease with many complicated care requirements and there is an urgent need to improve the cost and clinical effectiveness of cancer care pathways. Process mining of the e-health records of cancer patients may play an important future role and this paper presents a literature review of process mining in oncology as a contribution to this research. The search produced 758 articles which were manually reviewed by title, abstract, and full paper text review to develop the original pool of papers. An in-depth ancestor search was used to gather additional articles from the references of the original pool. These steps resulted in 37 papers. Through a thematic review process, the papers were analysed and five themes emerged. These were: 1) process and data types; 2) research questions; 3) techniques, perspectives and tools; 4) methodologies; 5) limitations and future work. This review can: (i) highlight the potential value of process mining for improving cancer care processes (ii) provide a useful overview of the current work undertaken; (iii) help researchers to choose process mining algorithms, techniques, tools, methodologies and approaches; and (iv) identify research opportunities in this new field of study.

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Section: Process Mining In Oncologymentioning

confidence: 99%

Process mining in oncology: A literature review

Kurniati

Johnson

Hogg³

et al. 2016

2016 6th International Conference on Information Communication and Management (ICICM)

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“…The evaluation of the quality of event logs in process mining relies on trustworthiness (recorded events actually happened), completeness and well defined semantics [56]. These can be achieved by selecting pathways with required data points using the pathways framework.…”

Section: Knowledge Discovery Supportmentioning

confidence: 99%

“…The visualisation system allows for the close inspection and contextualisation of pathways, illustrating particular paths with similar features. It has been reported that a combination of visual analytics with automated process mining techniques would make possible the extraction of more novel insights from event data [56] and further work in this area is needed.…”

Section: Knowledge Discovery Supportmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

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Abstract. Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

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“…See [14] for logging guidelines and details on getting event data from databases using redo logs. It is apparent that a condition sine qua non for the application of process mining is the availability of faithful event logs, adhering to accepted standards (such as XES) and guaranteeing a certain quality level [15]. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology.…”

Section: Introductionmentioning

confidence: 99%

Ontology-Driven Extraction of Event Logs from Relational Databases

Calvanese

Montali

Syamsiyah

et al. 2016

Business Process Management Workshops

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Abstract. Process mining is an emerging discipline whose aim is to discover, monitor and improve real processes by extracting knowledge from event logs representing actual process executions in a given organizational setting. In this light, it can be applied only if faithful event logs, adhering to accepted standards (such as XES), are available. In many real-world settings, though, such event logs are not explicitly given, but are instead implicitly represented inside legacy information systems of organizations, which are typically managed through relational technology. In this work, we devise a novel framework that supports domain experts in the extraction of XES event log information from legacy relational databases, and consequently enables the application of standard process mining tools on such data. Differently from previous work, the extraction is driven by a conceptual representation of the domain of interest in terms of an ontology. On the one hand, this ontology is linked to the underlying legacy data leveraging the well-established ontology-based data access (OBDA) paradigm. On the other hand, our framework allows one to enrich the ontology through user-oriented log extraction annotations, which can be flexibly used to provide different log-oriented views over the data. Different data access modes are then devised so as to view the legacy data through the lens of XES.

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Process Mining Manifesto

Cited by 747 publications

References 2 publications

Process mining in oncology: A literature review

Process mining in oncology: A literature review

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Ontology-Driven Extraction of Event Logs from Relational Databases

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