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.

scholarly journals CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 161
Author(s):  
Ghada Elkhawaga ◽  
Mervat Abuelkheir ◽  
Sherif I. Barakat ◽  
Alaa M. Riad ◽  
Manfred Reichert
Keyword(s):  
Systematic Review ◽  
Business Process ◽  
Continuous Monitoring ◽  
Business Processes ◽  
Process Mining ◽  
Concept Drift ◽  
Business Environments ◽  
Drift Analysis ◽  
Comprehensive Framework ◽  
Over Time

Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.

Concept drift detection and localization in process mining

2021 ◽  
Author(s):  
Rafael Gaspar de Sousa ◽  
Sarajane Marques Peres ◽  
Marcelo Fantinato ◽  
Hajo Alexander Reijers
Keyword(s):  
Process Mining ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Detection And Localization

Process Duration Modelling and Concept Drift Detection for Business Process Mining

2021 ◽  
Author(s):  
Lingkai Yang ◽  
Sally McClean ◽  
Mark Donnelly ◽  
Kevin Burke ◽  
Kashaf Khan
Keyword(s):  
Business Process ◽  
Process Mining ◽  
Concept Drift ◽  
Process Duration ◽  
Concept Drift Detection ◽  
Business Process Mining

scholarly journals A Framework for Explainable Concept Drift Detection in Process Mining

2021 ◽  
pp. 400-416
Author(s):  
Jan Niklas Adams ◽  
Sebastiaan J. van Zelst ◽  
Lara Quack ◽  
Kathrin Hausmann ◽  
Wil M. P. van der Aalst ◽  
...  
Keyword(s):  
Process Mining ◽  
Concept Drift ◽  
Concept Drift Detection

Comparing Concept Drift Detection with Process Mining Tools

2019 ◽  
Author(s):  
Nicolas Jashchenko Omori ◽  
Gabriel Marques Tavares ◽  
Paolo Ceravolo ◽  
Sylvio Barbon
Keyword(s):  
Process Mining ◽  
Concept Drift ◽  
Concept Drift Detection ◽  
Mining Tools

scholarly journals Comparing Concept Drift Detection with Process Mining Software

2020 ◽  
Vol 13 (4) ◽  
pp. 101-125
Author(s):  
Nicolas Jashchenko Omori ◽  
Gabriel Marques Tavares ◽  
Paolo Ceravolo ◽  
Sylvio Barbon Jr
Keyword(s):  
Business Processes ◽  
Process Mining ◽  
Concept Drift ◽  
Relevant Information ◽  
Process Control System ◽  
Process Data ◽  
Advantages And Disadvantages ◽  
Depth Analysis ◽  
Changes Over Time ◽  
Concept Drifts

Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use.

scholarly journals Clinical characterization of dysautonomia in long COVID-19 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nicolas Barizien ◽  
Morgan Le Guen ◽  
Stéphanie Russel ◽  
Pauline Touche ◽  
Florent Huang ◽  
...  
Keyword(s):  
Mixed Models ◽  
Repeated Measures ◽  
Physical Medicine ◽  
Persistent Symptoms ◽  
Index Changes ◽  
Changes Over Time ◽  
And Control ◽  
Testing Group ◽  
Over Time

AbstractIncreasing numbers of COVID-19 patients, continue to experience symptoms months after recovering from mild cases of COVID-19. Amongst these symptoms, several are related to neurological manifestations, including fatigue, anosmia, hypogeusia, headaches and hypoxia. However, the involvement of the autonomic nervous system, expressed by a dysautonomia, which can aggregate all these neurological symptoms has not been prominently reported. Here, we hypothesize that dysautonomia, could occur in secondary COVID-19 infection, also referred to as “long COVID” infection. 39 participants were included from December 2020 to January 2021 for assessment by the Department of physical medicine to enhance their physical capabilities: 12 participants with COVID-19 diagnosis and fatigue, 15 participants with COVID-19 diagnosis without fatigue and 12 control participants without COVID-19 diagnosis and without fatigue. Heart rate variability (HRV) during a change in position is commonly measured to diagnose autonomic dysregulation. In this cohort, to reflect HRV, parasympathetic/sympathetic balance was estimated using the NOL index, a multiparameter artificial intelligence-driven index calculated from extracted physiological signals by the PMD-200 pain monitoring system. Repeated-measures mixed-models testing group effect were performed to analyze NOL index changes over time between groups. A significant NOL index dissociation over time between long COVID-19 participants with fatigue and control participants was observed (p = 0.046). A trend towards significant NOL index dissociation over time was observed between long COVID-19 participants without fatigue and control participants (p = 0.109). No difference over time was observed between the two groups of long COVID-19 participants (p = 0.904). Long COVID-19 participants with fatigue may exhibit a dysautonomia characterized by dysregulation of the HRV, that is reflected by the NOL index measurements, compared to control participants. Dysautonomia may explain the persistent symptoms observed in long COVID-19 patients, such as fatigue and hypoxia. Trial registration: The study was approved by the Foch IRB: IRB00012437 (Approval Number: 20-12-02) on December 16, 2020.

scholarly journals Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 349-371
Author(s):  
Hassan Mehmood ◽  
Panos Kostakos ◽  
Marta Cortes ◽  
Theodoros Anagnostopoulos ◽  
Susanna Pirttikangas ◽  
...  
Keyword(s):  
Real World ◽  
Data Streams ◽  
Smart City ◽  
Smart Cities ◽  
Concept Drift ◽  
Distributed Environment ◽  
Real World Data ◽  
Unique Challenge ◽  
World Data ◽  
Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

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