Mining Process Mining Practices: An Exploratory Characterization of Information Needs in Process Analytics

2019 ◽  
pp. 322-337
Author(s):  
Christopher Klinkmüller ◽  
Richard Müller ◽  
Ingo Weber
Keyword(s):  
Information Needs ◽  
Process Mining ◽  
Process Analytics

Process Mining on FHIR - An Open Standards-Based Process Analytics Approach for Healthcare

2021 ◽  
pp. 343-355
Author(s):  
Emmanuel Helm ◽  
Oliver Krauss ◽  
Anna Lin ◽  
Andreas Pointner ◽  
Andreas Schuler ◽  
...  
Keyword(s):  
Process Mining ◽  
Process Analytics ◽  
Open Standards

scholarly journals Online concept drift detection, localization andcharacterization using trace clustering

2020 ◽  
Author(s):  
Rafael Gaspar De Sousa ◽  
Sarajane Marques Peres
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Concept Drift ◽  
Stationary Processes ◽  
Online Detection ◽  
Complex Problems ◽  
Online Setting ◽  
Concept Drift Detection ◽  
Over Time

Most process mining techniques assume stationary processes and are not well equipped to deal with concept drift. Online detection, localization and characterization of concept drift in business processes can support process mining techniques and analysts to improve organizations flexibility and adaptability. In this research, we propose a method to detect, locate and characterize concept drift in an online setting using trace clustering. The hypothesis is that the method can benefit from the trace clustering capacity to simplify complex problems through grouping similar patterns. In preliminary experiments, trace clustering was performed in a windowing setting showing that concept drift can be detected by analyzing the variation of clustering over time.

Morphological and molecular characterization of Castniidae (Lepidoptera) associated to sugarcane in Colombia

2021 ◽  
pp. 1-12
Author(s):  
Viviana Marcela Aya ◽  
Alejandro Pabón ◽  
Jorge M. González ◽  
Germán Vargas
Keyword(s):  
Information Needs ◽  
Genetic Variance ◽  
Haplotype Diversity ◽  
Taxonomic Status ◽  
Geographical Isolation ◽  
Alternative Hosts ◽  
Country Analysis ◽  
Valle Del Cauca ◽  
Morphological And Molecular Characterization

Abstract The giant sugarcane borer, Telchin licus, has been reported as an economically important sugarcane pest in Colombia; however, its taxonomic status has been scarcely investigated and previous reports offer an ambiguous characterization of both the immature and adult stages. The objective of this work is to identify Telchin species affecting sugarcane and alternative hosts in different departments of the country by integrating molecular analysis and conventional morphology. To date, T. licus has been found in the departments of Caquetá, Casanare, and Meta, while T. atymnius has been found in Antioquia, Caldas, Nariño, and Valle del Cauca. Sugarcane, Musaceae, and Heliconiaceae have been found to be hosts to both species. Additionally, the species T. cacica has also been registered in the department of Nariño, affecting heliconias and plantains. Genetic variation within the species allowed differentiation at the molecular level of subspecies of T. licus and T. atymnius, confirming that the subspecies present in Colombia are T. licus magdalena, T. atymnius humboldti, and T. atymnius atymnius. The haplotype diversity of populations is closely related to their geographical distribution, indicating low gene flow between populations and possible speciation inside the country. Analysis of genetic variance showed significant differences among and within T. atymnius populations, which may suggest a high genetic structure along the regions where it is found and the possible presence of additional subspecies to those previously reported. To understand the geographical and environmental conditions that determine the pest's distribution in Colombia, this information needs to be complemented with ecological considerations of possible geographical isolation and association of alternative hosts.

scholarly journals Toward Value-Based Healthcare through Interactive Process Mining in Emergency Rooms: The Stroke Case

2019 ◽  
Vol 16 (10) ◽  
pp. 1783 ◽  
Author(s):  
Gema Ibanez-Sanchez ◽  
Carlos Fernandez-Llatas ◽  
Antonio Martinez-Millana ◽  
Angeles Celda ◽  
Jesus Mandingorra ◽  
...  
Keyword(s):  
Value Chain ◽  
Health Professionals ◽  
Data Science ◽  
Process Mining ◽  
Emergency Rooms ◽  
Stroke Patients ◽  
Stroke Treatment ◽  
Black Boxes ◽  
Critical Timing

The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.

scholarly journals PRANAS: A Process Analytics System Based on Process Warehouse and Cube for Supply Chain Management

2020 ◽  
Vol 10 (10) ◽  
pp. 3521
Author(s):  
Aekyung Kim ◽  
Josue Obregon ◽  
Jae-Yoon Jung
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Reference Model ◽  
Process Mining ◽  
Chain Management ◽  
Process Analytics ◽  
Point Analysis ◽  
Case Type ◽  
Standard Models ◽  
Process Oriented

Most organizations need to monitor and assess their business activities. In order to support the performance analysis of the business activities in a more systematic manner, in this research, we introduce a PRocess ANalytics System, called PRANAS. The system adopts process warehouses and process cubes to support process-oriented analysis, as well as data-oriented analysis. In this research, the process warehouse and cube were designed to assess business performances for supply chain management, specifically under the SCOR standard models. Furthermore, the process cube was constructed based on process-related dimensions such as time, case type, and event class to support process mining. Finally, we exemplify how the system can be applied to process analytics with three use cases of process discovery, data analytics, and decision point analysis. It is expected that the proposed system can be a helpful reference model when business process analyst designs process analytics systems in the process-oriented perspective, as well as in the data-oriented perspective.

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