scholarly journals Data Mining for Process Modeling: A Clustered Process Discovery Approach

2020 ◽  
Author(s):  
Renato Cirne ◽  
Caio Melquiades ◽  
Renan Leite ◽  
Eronita Leijden ◽  
Alexandre Maciel ◽  
...  
Keyword(s):  
Data Mining ◽  
Process Modeling ◽  
Process Discovery

scholarly journals Effort Estimation of Business Process Modeling through Clustering Techniques

2014 ◽  
Vol 7 (1) ◽  
pp. 34-47
Author(s):  
Fernanda Baiao ◽  
Kate Revoredo ◽  
Brunno Silveira ◽  
Felipe Klussmann
Keyword(s):  
Data Mining ◽  
Business Process ◽  
Process Modeling ◽  
Real Data ◽  
Business Process Modeling ◽  
Project Planning ◽  
Effort Estimation ◽  
Useful Knowledge ◽  
Critical Activity ◽  
Using Data

A critical activity in project planning, especially in business process modeling (BPM) projects, is effort estimation. It involves several dimensions such as business domain complexity, team and technology characteristics, turning estimation into a difficult and inaccurate task. In order to reduce this difficulty, background knowledge about past projects is typically applied; however, it is too costly to be carried out manually. On the other hand, Data Mining enables the automatic extraction of new nontrivial and useful knowledge from existing data. This paper presents a new approach for BPM project effort estimation using data mining through clustering technique. This approach was successfully applied to real data

Business Process Intelligence

2011 ◽  
pp. 456-480 ◽  
Author(s):  
M. Castellanos ◽  
A.K. Alves de Medeiros ◽  
J. Mendling ◽  
B. Weber ◽  
A.J.M.M. Weijters
Keyword(s):  
Business Process ◽  
Process Modeling ◽  
Business Processes ◽  
Process Mining ◽  
Process Efficiency ◽  
Conformance Checking ◽  
Process Redesign ◽  
Process Discovery ◽  
Business Process Intelligence ◽  
Introductory Overview

Business Process Intelligence (BPI) is an emerging area that is getting increasingly popular for enterprises. The need to improve business process efficiency, to react quickly to changes and to meet compliance is among the main drivers for BPI. BPI refers to the application of Business Intelligence techniques to business processes and comprises a large range of application areas spanning from process monitoring and analysis to process discovery, conformance checking, prediction and optimization. This chapter provides an introductory overview of BPI and its application areas and delivers an understanding of how to apply BPI in one’s own setting. In particular, it shows how process mining techniques such as process discovery and conformance checking can be used to support process modeling and process redesign. In addition, it illustrates how processes can be improved and optimized over time using analytics for explanation, prediction, optimization and what-if-analysis. Throughout the chapter, a strong emphasis is given to describe tools that use these techniques to support BPI. Finally, major challenges for applying BPI in practice and future trends are discussed.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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