Process Mining as the Superglue Between Data Science and Enterprise Computing

2014 ◽  
Author(s):  
Wil Van Der Aalst
Keyword(s):  
Data Science ◽  
Process Mining

scholarly journals Analysing Structured Learning Behaviour in Massive Open Online Courses (MOOCs): An Approach Based on Process Mining and Clustering

2018 ◽  
Vol 19 (5) ◽  
Author(s):  
Antoine Van den Beemt ◽  
Joos Buijs ◽  
Wil Van der Aalst
Keyword(s):  
Data Science ◽  
Online Courses ◽  
Process Mining ◽  
Massive Open Online Courses ◽  
Structured Learning ◽  
Learning Behaviour ◽  
Specific Behaviour ◽  
Learning Progress ◽  
Tools And Techniques ◽  
Successful Students

The increasing use of digital systems to support learning leads to a growth in data regarding both learning processes and related contexts. Learning Analytics offers critical insights from these data, through an innovative combination of tools and techniques. In this paper, we explore students’ activities in a MOOC from the perspective of personal constructivism, which we operationalized as a combination of learning behaviour and learning progress. This study considers students’ data analyzed as per the MOOC Process Mining: Data Science in Action. We explore the relation between learning behaviour and learning progress in MOOCs, with the purpose to gain insight into how passing and failing students distribute their activities differently along the course weeks, rather than predict students' grades from their activities. Commonly-studied aggregated counts of activities, specific course item counts, and order of activities were examined with cluster analyses, means analyses, and process mining techniques. We found four meaningful clusters of students, each representing specific behaviour ranging from only starting to fully completing the course. Process mining techniques show that successful students exhibit a more steady learning behaviour. However, this behaviour is much more related to actually watching videos than to the timing of activities. The results offer guidance for teachers.

scholarly journals A Systematic Review of Recommendations of Long-Term Strategies for Researchers Using Data Science Techniques

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 35
Author(s):  
Gilberto Ayala-Bastidas ◽  
Hector G. Ceballos ◽  
Francisco J. Cantu-Ortiz
Keyword(s):  
Systematic Review ◽  
Data Science ◽  
Process Mining ◽  
Research Strategies ◽  
Scientific Content ◽  
Open Research ◽  
Using Data ◽  
Long Term Impact ◽  
The Impact

The impact of the strategies that researchers follow to publish or produce scientific content can have a long-term impact. Identifying which strategies are most influential in the future has been attracting increasing attention in the literature. In this study, we present a systematic review of recommendations of long-term strategies in research analytics and their implementation methodologies. The objective is to present an overview from 2002 to 2018 on the development of this topic, including trends, and addressed contexts. The central objective is to identify data-oriented approaches to learn long-term research strategies, especially in process mining. We followed a protocol for systematic reviews for the engineering area in a structured and respectful manner. The results show the need for studies that generate more specific recommendations based on data mining. This outcome leaves open research opportunities from two particular perspectives—applying methodologies involving process mining for the context of research analytics and the feasibility study on long-term strategies using data science techniques.

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.

Gaussian Representations of K-Means Clusters: Case Study of Educational Process Mining of UCI

2020 ◽  
Author(s):  
Yu-Chien Ko ◽  
Hamido Fujita
Keyword(s):  
Data Science ◽  
Process Mining ◽  
Data Representation ◽  
Iterative Learning ◽  
Educational Process ◽  
Learning Approach ◽  
Inside Information ◽  
Strategic Information ◽  
Gaussian Representation

The information of data patterns can help determining analytical direction, choosing right tools for analysis, and validating inferential results. However, this argument might not be helpful because of diverse patterns. To disclose inside information, a learning approach about data clustering is proposed by integrating K-means and Gaussian representation from data science. It gets insight of similar and dominant distribution through iterative learning. Its core technique lies in the design of data representation which can carry similarity and dominance characteristics from samples to K-learning. For illustration, it is applied in the educational process mining of UCI. Its results can provide strategic information for educational activities.

scholarly journals Getting There: Evidence-Based Decision-Making in Road Trauma Prehospital Transport and Care in Queensland

2019 ◽  
Vol 34 (s1) ◽  
pp. s64-s65
Author(s):  
Robert Andrews ◽  
Moe Wynn ◽  
Arthur ter Hofstede ◽  
Kirsten Vallmuur ◽  
Emma Bosley ◽  
...  
Keyword(s):  
Emergency Department ◽  
Data Quality ◽  
Data Science ◽  
Hospital Admissions ◽  
Process Mining ◽  
Transport Processes ◽  
Process Models ◽  
Actual Behavior ◽  
Patient Pathways ◽  
Quality Issues

Introduction:Process mining, a branch of data science, aims at deriving an understanding of process behaviors from data collected during executions of the process. In this study, we apply process mining techniques to examine retrieval and transport of road trauma patients in Queensland. Specifically, we use multiple datasets collected from ground and air ambulance, emergency department, and hospital admissions to investigate the various patient pathways and transport modalities from accident to definitive care.Aim:The project aims to answer the question, “Are we providing the right level of care to patients?” We focus on (i) automatically discovering, from historical records, the different care and transport processes, and (ii) identifying and quantifying factors influencing deviance from standard processes, e.g. mechanisms of injury and geospatial (crash and trauma facility) considerations.Methods:We adapted the Cross-Industry Standard Process for Data Mining methodology to Queensland Ambulance Service, Retrieval Services Queensland (aero-medical), and Queensland Health (emergency department and hospital admissions) data. Data linkage and “case” definition emerged as particular challenges. We developed detailed data models, conduct a data quality assessment, and preliminary process mining analyses.Results:Preliminary results only with full results are presented at the conference. A collection of process models, which revealed multiple transport pathways, were automatically discovered from pilot data. Conformance checking showed some variations from expected processing. Systematic analysis of data quality allowed us to distinguish between systemic and occasional quality issues, and anticipate and explain certain observable features in process mining analyses. Results will be validated with domain experts to ensure insights are accurate and actionable.Discussion:Preliminary analysis unearthed challenging data quality issues that impact the use of historical retrieval data for secondary analysis. The automatically discovered process models will facilitate comparison of actual behavior with existing guidelines.

scholarly journals Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process

2019 ◽  
Vol 16 (2) ◽  
pp. 199 ◽  
Author(s):  
Antonio Martinez-Millana ◽  
Aroa Lizondo ◽  
Roberto Gatta ◽  
Salvador Vera ◽  
Vicente Salcedo ◽  
...  
Keyword(s):  
Analytic Hierarchy Process ◽  
Real Time ◽  
Data Science ◽  
Process Management ◽  
Process Mining ◽  
Operating Rooms ◽  
Analytic Hierarchy ◽  
Software Applications ◽  
Hierarchy Process ◽  
Location Systems

The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification
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