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Detecting Changes in Process Behavior Using Comparative Case Clustering [chapter]

B. F. A. Hompes, J. C. A. M. Buijs, Wil M. P. van der Aalst, P. M. Dixit, J. Buurman
2017 Lecture Notes in Business Information Processing  
it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ?  ...  Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  ...  Differences in behavior can be compared using comparative case clustering. Hence, clustering of cases is not limited to finding similar execution paths.  ... 
doi:10.1007/978-3-319-53435-0_3 fatcat:as2juscsazbbtpei3n32ccd3ou

A Survey on Concept Drift in Process Mining

Denise Maria Vecino Sato, Sheila Cristiana De Freitas, Jean Paul Barddal, Edson Emilio Scalabrin
2022 ACM Computing Surveys  
Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version.  ...  We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online  ...  This approach is not listed in the clustering approaches because clustering is not used to detect the drift but to analyze the effect of the changing behavior.  ... 
doi:10.1145/3472752 fatcat:zay6purz35bsfejb3zdpn3yb4m

Comprehensive Process Drift Detection with Visual Analytics [article]

Anton Yeshchenko and Claudio Di Ciccio and Jan Mendling and Artem Polyvyanyy
2019 arXiv   pre-print
The technique starts by clustering declarative process constraints discovered from recorded logs of executed business processes based on their similarity and then applies change point detection on the  ...  Recent research has introduced ideas from concept drift into process mining to enable the analysis of changes in business processes over time.  ...  Next, using change point detection techniques, we identify the process drifts, i.e., the points in which significant changes in the confidence of behavioral rules occur.  ... 
arXiv:1907.06386v1 fatcat:z4mpjc6dang7zmql7cih7bxuni

Unsupervised density-based behavior change detection in data streams

Rosane M.M. Vallim, José A. Andrade Filho, Rodrigo F. de Mello, André C. P. L. F. de Carvalho, João Gama
2014 Intelligent Data Analysis  
The ability to detect changes in the data distribution is an important issue in Data Stream mining.  ...  This paper proposes a framework for non-supervised automatic change detection in Data Streams called M-DBScan.  ...  This article is concerned with behavior change detection on unsupervised scenarios. Behavior change detection in such case is performed in a variety of ways [2, 17, 4, 16] .  ... 
doi:10.3233/ida-140636 fatcat:mefiwkwpingfpfauernyngxrle

Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

Wei Zhang, Xiaowei Dong, Huaibao Li, Jin Xu, Dan Wang
2020 IEEE Access  
evaluation indexes, is used to detect abnormal electricity consumption behaviors.  ...  After that, in the abnormal detection step, a density-based clustering algorithm, in which the best clustering parameters are selected through iteration and evaluation, combined with unsupervised clustering  ...  At the same time, it can detect outliers in the process of clustering.  ... 
doi:10.1109/access.2020.2980079 fatcat:ldz2dbszdnetdgyixuvff7vc5a

A comparative evaluation of intrusion detection architectures for mobile ad hoc networks

Christos Xenakis, Christoforos Panos, Ioannis Stavrakakis
2011 Computers & security  
It cannot detect attacks that do not modify or drop packets remains cluster-heads increases the processing and communication overhead structures that are robust to network changes create new security risks  ...  malicious behaviors.  ...  Extensive changes in this table may be a symptom of malicious behaviors that attempt to disrupt the routing process.  ... 
doi:10.1016/j.cose.2010.10.008 fatcat:d33lfntp6re7jgaplifpwaontu

Visualizing Business Process Evolution [chapter]

Anton Yeshchenko, Dina Bayomie, Steven Gross, Jan Mendling
2020 Lecture Notes in Business Information Processing  
Existing process drift approaches focus to a great extent on drift point detection, i.e., on points in time when a process execution changes significantly.  ...  What is largely neglected by process drift approaches is the identification of temporal dynamics of different clusters of process execution, how they interrelate, and how they change in dominance over  ...  To address this dynamism, recent research aims to identify changes in processes over time. Most prominently, process drift uses event logs to detect points in time when changes take place [1, 5] .  ... 
doi:10.1007/978-3-030-49418-6_12 fatcat:6646v3jarvfclimpouiwqu2bvq

Visual Drift Detection for Event Sequence Data of Business Processes [article]

Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, Artem Polyvyanyy
2021 arXiv   pre-print
An open challenge is the visual analysis of drift phenomena when processes change over time. In this paper, we address this research gap.  ...  Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts.  ...  In the worst case, the change point detection algorithm has a quadratic performance [61] in the number of time series in the cluster Op|D| 2 q " p# 2 cns q.  ... 
arXiv:2011.09130v2 fatcat:mxsbnlk72jg3piycciiyfzv6ne

Anomaly detection algorithm based on life pattern extraction from accumulated pyroelectric sensor data

T. Mori, R. Urushibata, M. Shimosaka, H. Noguchi, T. Sato
2008 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems  
Using this function, we try to classify behavior labels and detect anomaly. Here, we assume two kinds of anomaly, "the rare behaviors" and "the changes of life pattern".  ...  This paper describes an algorithm of behavior labeling and anomaly detection for elder people living alone.  ...  In order to realize such system in the near future, we have been using a sensor system that is comparatively cheap [3] . Concretely, we decide to use a few pyroelectric sensors per one house.  ... 
doi:10.1109/iros.2008.4650864 dblp:conf/iros/MoriUSNS08 fatcat:mktpe2figrh7hmwwb6lbnbvekq

Learning Algorithms in the Detection of Unused Functionalities in SOA Systems [chapter]

Ilona Bluemke, Marcin Tarka
2013 Lecture Notes in Computer Science  
Two algorithms: K-means clustering and Kohonen networks were used to detect the unused functionalities and the results of this experiment are discussed.  ...  In this system several anomalies were introduced and the effectiveness of algorithms in detecting them were measured.  ...  Experiment The research system presented in section 3 was used to explore four cases typical for SOA systems i.e.: change in the frequency of service calls, change in the frequency of a group of services  ... 
doi:10.1007/978-3-642-40925-7_36 fatcat:pwhsq5rv7jfinnviuj2nxim4z4

Clustering in wavelet domain: A multiresolution ART network for anomaly detection

Hrishikesh B. Aradhye, Bhavik R. Bakshi, James F. Davis, Stanley C. Ahalt
2004 AIChE Journal  
The multiscale ART-2 (MSART-2) algorithm detects a process change when one or more wavelet coefficients violate the similarity thresholds with respect to clusters of wavelet coefficients under normal process  ...  Comparative results on real industrial case studies from a petrochemical process plant are also presented.  ...  the issue of stable adaptation and incremental learning with changing process behavior.  ... 
doi:10.1002/aic.10245 fatcat:6zvpyei3czbuzm4llj75iyzz6i

A Stable and Online Approach to Detect Concept Drift in Data Streams

Fausto Guzzo da Costa, Rodrigo Fernandes de Mello
2014 2014 Brazilian Conference on Intelligent Systems  
By holding a stability property, clustering algorithms would guarantee that a change in clustering models correpond to actual changes in input data.  ...  Experiments were conducted using synthetic data streams under different behaviors. Results confirm this new approach is capable of detecting concept drift in data streams.  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of CAPES, FAPESP and CNPq.  ... 
doi:10.1109/bracis.2014.66 dblp:conf/bracis/CostaM14 fatcat:xzntis52wrhldf7j5zjhc2h5w4

Two-Stage Orthogonal Network Incident Detection for the Adaptive Coordination with SMTP Proxy [chapter]

Ruo Ando, Yoshiyasu Takefuji
2003 Lecture Notes in Computer Science  
The proposal method is able to correct false positive of AID module for the unusual changes in the system by the subsequent misuse detection learning labeled data.  ...  In this paper we present the adaptive detection and coordination system, two-stage detection, which consists of anomaly and misuse detection combined by lightweight neural networks to synchronize with  ...  The other research is to compare the anomaly detection and misuse detection in monitoring system or network behavior.  ... 
doi:10.1007/978-3-540-45215-7_37 fatcat:dk3iihpoyfemrf35zlraxhicoq

Implementation of a Hierarchical Hybrid Intrusion Detection Mechanism in Wireless Sensors Network

Lamyaa Moulad, Hicham Belhadaoui, Mounir Rifi
2017 International Journal of Advanced Computer Science and Applications  
In this regard, we proposed an integral mechanism which is an hybrid Intrusion Detection approach based on anomaly, detection using support vector machine (SVM), specifications based technique, signature  ...  and abnormal behaviors of intruders, hence the goal of this paper.  ...  detection module: In case of abnormal behavior the IDs send an alarm to the rest of components, and remove the intruder.  ... 
doi:10.14569/ijacsa.2017.081035 fatcat:pumgqzgsojc6pp2xgopsvuscvq

A Density-Based Clustering Approach for Behavior Change Detection in Data Streams

Rosane M.M. Vallim, Jose A. Andrade Filho, Andre C.P.L.F. Carvalho, Joao Gama
2012 2012 Brazilian Symposium on Neural Networks  
Experimental results using synthetic data provide insight on how clustering and novelty detection algorithms can be used for change detection in data streams.  ...  It suggests the use of density-based clustering and an entropy measurement for change detection that is independent of the number and format of clusters.  ...  For this case, clustering techniques are commonly used for analyzing the stream and trying to figure out if, and when, data behavior is changing.  ... 
doi:10.1109/sbrn.2012.22 dblp:conf/sbrn/VallimFCG12 fatcat:m3bpgyk2djf7fpblnli2aurxgu
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