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Concept Drift Detection Of Event Streams Using An Adaptive Window
2019
ECMS 2019 Proceedings edited by Mauro Iacono, Francesco Palmieri, Marco Gribaudo, Massimo Ficco
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In this paper, we introduce an efficient approach that uses the collected information of an event stream miner to detect concept drifts. ...
This initiated the field of stream process mining very recently. Drifts in the underlying concepts of the business processes are of a great interest for decision makers. ...
The author would like to thank Prof. Thomas Seidl for the useful discussions through various phases of this paper and Florian Richter for the implementation of some parts of StrP-toMCDD. ...
doi:10.7148/2019-0230
dblp:conf/ecms/Hassani19
fatcat:zttahsxfuzaovmfl6u3nyintnu
CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining
2020
Algorithms
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A four-staged framework providing guidance on the fundamental components of a concept drift analysis approach in the context of process mining (CONDA-PM,)) framework describing phases and requirements ...
This article proposes the CONcept Drift Analysis in Process Mining (CONcept Drift Analysis in Process Mining Framework. ...
Concept Drift and Process Drift In [9] , the authors define business process drift detection as "a family of techniques to analyse event logs or event streams generated during the execution of a business ...
doi:10.3390/a13070161
fatcat:jne6xqmz3fbpzfsx56laixop4i
Moving Beyond Traditional KPIs: Data-Driven Performance Monitoring in Operations
2022
Zenodo
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An end-to-end monitoring approach allows for better system understanding and opens the door for more efficient root cause investigations when anomalies occur. ...
To prepare for future operations, Paranal established a dedicated data & system analysis framework, investigating different operational scenarios, testing new techniques and technologies. ...
monitored to detect and drift in performance, or any significant change in input values • Lifetime of the model must be considered, and periodic retraining of the model must be scheduled • Project Justification ...
doi:10.5281/zenodo.6565933
fatcat:qfjhesiqkzciha5xic2k6pidq4
Detecting Sudden and Gradual Drifts in Business Processes from Execution Traces
2017
IEEE Transactions on Knowledge and Data Engineering
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Business process drift detection refers to a family of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. ...
This paper proposes an automated and statistically grounded method for detecting sudden and gradual business process drifts under a unified framework. ...
Business process drift detection [1] - [3] is a family of techniques to analyze event logs or event streams generated during the execution of a business process in order to detect points in time when ...
doi:10.1109/tkde.2017.2720601
fatcat:5xaxcndmbnfmbbkv5g7out343u
An Overview of Concept Drift Applications
[chapter]
2015
Studies in Big Data
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Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift. ...
First we overview and categorize application tasks for which the problem of concept drift is particularly relevant. ...
This work was partially supported by European Commission through the project MAESTRA (Grant number ICT-2013-612944). ...
doi:10.1007/978-3-319-26989-4_4
fatcat:nckbz7bk4natlpnkx37co4dznu
Pattern Discovery of User Interface Sequencing by Rehabilitation Clients with Cognitive Impairments
2012
2012 45th Hawaii International Conference on System Sciences
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This paper introduces theory and design of stream sequence-mining for UI event streams. 45th Hawaii International Conference on System Sciences 978-0-7695-4525-7/12 $26.00 ...
By continuously monitoring usage, the project is able continually adapt the software to a user's changing needs. Thus, monitoring software usage, particularly email event sequences, is important. ...
Detecting changes in data-streams is important for monitoring, in particular for AT monitoring systems. ...
doi:10.1109/hicss.2012.467
dblp:conf/hicss/RobinsonSAD12
fatcat:uofjctlt65c7rcl57z6575owji
Architecture Proposal for Machine Learning Based Industrial Process Monitoring
2020
Procedia Computer Science
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Fourth, human crafted alarm rules can now also include a learning process to improve these rules, for example by using active learning with a human-in-the-loop approach. ...
Fourth, human crafted alarm rules can now also include a learning process to improve these rules, for example by using active learning with a human-in-the-loop approach. ...
The front-end (block 11) is an application using the React JavaScript framework, where the operators create and manage the library of rules and logs of rule execution. ...
doi:10.1016/j.procs.2020.03.137
fatcat:mxg65fkiqjbyliayc5es5jzrwu
Adaptive provisioning of human expertise in service-oriented systems
2011
Proceedings of the 2011 ACM Symposium on Applied Computing - SAC '11
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The seamless integration of humans in the SOA loop triggers numerous social implications, such as evolving expertise and drifting interests of human service providers. ...
While most existing work focuses on the discovery and composition of software based services, we highlight concepts for a people-centric Web. ...
The authors thank Lukasz Juszczyk for providing the Genesis2 framework and supporting this work. ...
doi:10.1145/1982185.1982517
dblp:conf/sac/SkopikSPD11
fatcat:uae2wngbwfhxfphvxq43mfgco4
Dynamic Constructs Competition Miner - Occurrence- vs. Time-Based Ageing
[chapter]
2015
Lecture Notes in Business Information Processing
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In this paper we propose a set of modifications for the CCM to enable dynamic business process discovery of a run-time process model from a stream of events. ...
The basis for this paper is the Constructs Competition Miner (CCM): A divide-and-conquer algorithm which discovers block-structured processes from event logs possibly consisting of exceptional behaviour ...
Another approach for discovering concept drifts on event streams of less relevance to the paper's topic is presented in [12] : A discovery approach for declarative process models using the sliding window ...
doi:10.1007/978-3-319-27243-6_4
fatcat:a4bncbldmrdi7gbduwlal6m7fy
Event stream-based process discovery using abstract representations
2017
Knowledge and Information Systems
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We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. ...
A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. ...
In case of concept drift, the size of the internal data structure of use impacts both model quality and the drift detection point. ...
doi:10.1007/s10115-017-1060-2
fatcat:iunnvliqqrdidginn5k7q4cjie
A heterogeneous online learning ensemble for non-stationary environments
2019
Knowledge-Based Systems
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with concept drift. ...
This paper aims to investigate the benefits of online model selection for predictive modelling in nonstationary environments. ...
They use concept drift detection methods to determine whether a concept drift has occurred. When concept drift detection occurs, methods for dealing with concept drift are triggered. ...
doi:10.1016/j.knosys.2019.104983
fatcat:hrmfhkudovbz7bke3gppcsgdke
Ensemble learning for data stream analysis: A survey
2017
Information Fusion
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Besides presenting a comprehensive spectrum of ensemble approaches for data streams, we also discuss advanced learning concepts such as imbalanced data streams, novelty detection, active and semisupervised ...
Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. ...
A recent experimental framework for the drift detection evaluation can be found in [89] . ...
doi:10.1016/j.inffus.2017.02.004
fatcat:rfc735znxjcwdebcbjxbyx7xki
Machine Learning in Action: Examples
[chapter]
2015
Efficient Learning Machines
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Machine learning exploits the power of generalization, which is an inherent and essential component of concept formation through human learning. ...
A traditional system uses a collection of attributes that determine a property or behavior in current time. ...
: The measure of events monitored over a larger sampling period, with the objective of calculating the event behavior with a built-in repeatability (e.g., the count of logging attempts in a day). ...
doi:10.1007/978-1-4302-5990-9_11
fatcat:mdmgulxgljginjs7jdwjdxg7qe
Business Process Mining
[article]
2016
arXiv
pre-print
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The aim of process mining is to extract knowledge from event logs of today's organizational information systems. ...
Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area. ...
In incremental drift, a process will change in gradual fashion. Considering concept drift is important issue in dealing with real processes logs. ...
arXiv:1607.00607v1
fatcat:3mndidjjnrdvjcfh4hvzv7zup4
A Self-Healing Framework for Online Sensor Data
2015
2015 IEEE International Conference on Autonomic Computing
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A set of mechanisms for each and every step of the self-healing framework, covering detection, classification, and correction of faults are proposed. ...
We design a framework which provides self-healing capabilities, enabling a flexible choice of components for detection, classification, and correction of faults at runtime. ...
ACKNOWLEDGEMENT The work is supported by 1) the FP7-ICT-2013-EU-Japan, Collaborative project ClouT, EU FP7 Grant number 608641; NICT management number 167 and 2) the Dutch National Research Council Energy ...
doi:10.1109/icac.2015.61
dblp:conf/icac/Nguyen0YT15
fatcat:x4pjxlv6s5bs5kahqwmcct3jfq
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