Using Big Data to Improve Safety Performance: An Application of Process Mining to Enhance Data Visualisation

2021 ◽  
Vol 25 ◽  
pp. 100210
Author(s):  
Anastasiia Pika ◽  
Arthur H.M. ter Hofstede ◽  
Robert K. Perrons ◽  
Georg Grossmann ◽  
Markus Stumptner ◽  
...  
Keyword(s):  
Big Data ◽  
Process Mining ◽  
Safety Performance ◽  
Data Visualisation

Big Data Visualisation and Visual Analytics for Music Data Mining

2018 ◽  
Author(s):  
Katrina E. Barkwell ◽  
Alfredo Cuzzocrea ◽  
Carson K. Leung ◽  
Ashley A. Ocran ◽  
Jennifer M. Sanderson ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Visual Analytics ◽  
Data Visualisation ◽  
Music Data Mining

Intranet Exploitation of Social Network Knowledge Intelligence

2017 ◽  
pp. 273-298
Author(s):  
Mark Alan Underwood
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Life Cycle ◽  
Online Social Networks ◽  
Intelligent Agents ◽  
Process Mining ◽  
Just In Time ◽  
Domain Specific ◽  
Social Graphs

Intranets are almost as old as the concept of a web site. More than twenty-five years ago the text Business Data Communications closed with a discussion of intranets (Stallings, 1990). Underlying technology improvements in intranets have been incremental; intranets were never seen as killer developments. Yet the popularity of Online Social Networks (OSNs) has led to increased interest in the part OSNs play – or could play – in using intranets to foster knowledge management. This chapter reviews research into how social graphs for an enterprise, team or other collaboration group interacts with the ways intranets have been used to display, collect, curate and disseminate information over the knowledge life cycle. Future roles that OSN-aware intranets could play in emerging technologies, such as process mining, elicitation methods, domain-specific intelligent agents, big data, and just-in-time learning are examined.

Towards insight-driven sampling for big data visualisation

2019 ◽  
Vol 39 (7) ◽  
pp. 788-807 ◽  
Author(s):  
Moeti M. Masiane ◽  
Anne Driscoll ◽  
Wuchun Feng ◽  
John Wenskovitch ◽  
Chris North
Keyword(s):  
Big Data ◽  
Data Visualisation

scholarly journals Combining event- and variable-centred approaches to institution-facing learning analytics at the unit of study level

2017 ◽  
Vol 34 (1) ◽  
pp. 63-78 ◽  
Author(s):  
Nick Kelly ◽  
Maximiliano Montenegro ◽  
Carlos Gonzalez ◽  
Paula Clasing ◽  
Augusto Sandoval ◽  
...  
Keyword(s):  
Big Data ◽  
Learning Analytics ◽  
Process Mining ◽  
Approaches To Learning ◽  
Time Frame ◽  
Data Set ◽  
Content Type ◽  
Modelling Techniques ◽  
Level Of Understanding

Purpose The purpose of this paper is to demonstrate the utility of combining event-centred and variable-centred approaches when analysing big data for higher education institutions. It uses a large, university-wide data set to demonstrate the methodology for this analysis by using the case study method. It presents empirical findings about relationships between student behaviours in a learning management system (LMS) and the learning outcomes of students, and further explores these findings using process modelling techniques. Design/methodology/approach The paper describes a two-year study in a Chilean university, using big data from a LMS and from the central university database of student results and demographics. Descriptive statistics of LMS use in different years presents an overall picture of student use of the system. Process mining is described as an event-centred approach to give a deeper level of understanding of these findings. Findings The study found evidence to support the idea that instructors do not strongly influence student use of an LMS. It replicates existing studies to show that higher-performing students use an LMS differently from the lower-performing students. It shows the value of combining variable- and event-centred approaches to learning analytics. Research limitations/implications The study is limited by its institutional context, its two-year time frame and by its exploratory mode of investigation to create a case study. Practical implications The paper is useful for institutions in developing a methodology for using big data from a LMS to make use of event-centred approaches. Originality/value The paper is valuable in replicating and extending recent studies using event-centred approaches to analysis of learning data. The study here is on a larger scale than the existing studies (using a university-wide data set), in a novel context (Latin America), that provides a clear description for how and why the methodology should inform institutional approaches.

scholarly journals Process mining: data analytics voor de accountant die wil weten hoe het nu echt zit

2015 ◽  
Vol 89 (10) ◽  
pp. 359-368
Author(s):  
Wim van der Aalst ◽  
Angelique Koopman
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Process Mining ◽  
Event Data ◽  
Process Discovery ◽  
Compliance Checking

Steeds meer gebeurtenissen (“events”) worden geregistreerd en opgeslagen in IT-systemen. Op dit moment staat “Big Data” volop in de schijnwer- pers en denken we vaak aan bedrijven als Google en Facebook. Event data zijn ech- ter in elke organisatie te vinden en op elk niveau. Process mining is de verbindende schakel tussen data en proces. Dankzij process mining is het mogelijk tegelijkertijd prestatie-georiënteerde en compliance-georiënteerde vragen te stellen. Door pro- cesmodellen te koppelen aan event data kunnen knelpunten opgespoord worden en is precies te zien waar en waarom mensen afwijken van het normatieve proces. Dit artikel beschrijft twee basisvormen van process mining: ‘process discovery’ en ‘con- formance/compliance checking’.

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