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Detecting Drift from Event Streams of Unpredictable Business Processes [chapter]

Alireza Ostovar, Abderrahmane Maaradji, Marcello La Rosa, Arthur H. M. ter Hofstede, Boudewijn F. V. van Dongen
2016 Lecture Notes in Computer Science  
Existing business process drift detection methods do not work with event streams.  ...  We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams.  ...  Conclusion We presented a fully automated method for online detection of business process drifts from event streams.  ... 
doi:10.1007/978-3-319-46397-1_26 fatcat:q3kn6twdojezvn2i4z4shcpdxq

Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Asmae HMAMI, Hanae SBAI, Mounia FREDJ
2021 International Journal of Advanced Computer Science and Applications  
They exist many approaches to manage a collection of business process and deal with variability.  ...  Changes are random and unavoidable actions in business processes, and they are frequently overlooked by managers, especially when managers need to deal with a collection of process variants.  ...  Those steps are inspired by an existing algorithm to detect concept drift in a data stream called STAGGER.  ... 
doi:10.14569/ijacsa.2021.0120479 fatcat:ibnnvpwzxba5xnouv34ra4osya

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

Ghada Elkhawaga, Mervat Abuelkheir, Sherif I. Barakat, Alaa M. Riad, Manfred Reichert
2020 Algorithms  
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.  ...  This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it.  ...  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

An Overview of Concept Drift Applications [chapter]

Indrė Žliobaitė, Mykola Pechenizkiy, João Gama
2015 Studies in Big Data  
First we overview and categorize application tasks for which the problem of concept drift is particularly relevant.  ...  Then we construct a reference framework for positioning application tasks within a spectrum of problems related to concept drift.  ...  In Section 2 we discuss knowledge discovery process in the context of learning from streaming data and handling concept drift.  ... 
doi:10.1007/978-3-319-26989-4_4 fatcat:nckbz7bk4natlpnkx37co4dznu

Anomaly detection techniques for streaming data–An overview

Saranya Kunasekaran, Chellammal Suriyanarayanan
2020 Malaya Journal of Matematik  
With the advent of smart devices and the Internet, data is being generated from various sources including mobile phones, sensor networks, telecommunications, satellites, log data, business, health care  ...  In this paper, an overview of different techniques for detection of anomaly is presented.  ...  Processing continuous flow of data in real time is termed as stream processing and it needs more efficient techniques to handle both volume and velocity of the streams of event.  ... 
doi:10.26637/mjm0s20/0133 fatcat:blyjw2z4q5datacu7y4lavwchq

Challenges and Opportunities in Rapid Epidemic Information Propagation with Live Knowledge Aggregation from Social Media [article]

Calton Pu, Abhijit Suprem, Rodrigo Alves Lima
2020 arXiv   pre-print
The first (True Novelty) is the capture of new, previously unknown, information from unpredictably evolving situations. The second (Fact vs.  ...  However, the tests are both incomplete (due to untested asymptomatic cases) and late (due the lag from the initial contact event, worsening symptoms, and test results).  ...  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding  ... 
arXiv:2011.05416v1 fatcat:t3zj6fnnnbfxdoq7w7smww2h6y

Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models [chapter]

Fabrizio Maria Maggi, Andrea Burattin, Marta Cimitile, Alessandro Sperduti
2013 Lecture Notes in Computer Science  
The framework continuously updates a set of valid business constraints based on the events occurred in the event stream.  ...  In this paper, we present a novel framework for the discovery of LTL-based declarative process models from streaming event data in settings where it is impossible to store all events over an extended period  ...  In addition, we want to characterize the portions of the event stream in which the process behavior changes in terms of business constraints.  ... 
doi:10.1007/978-3-642-41030-7_7 fatcat:evql2vjzbzfixe5jms6rnx3clu

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management

Dinithi Nallaperuma, Rashmika Nawaratne, Tharindu Bandaragoda, Achini Adikari, Su Nguyen, Thimal Kempitiya, Daswin De Silva, Damminda Alahakoon, Dakshan Pothuhera
2019 IEEE transactions on intelligent transportation systems (Print)  
The STMP integrates the heterogeneous big data streams, such as the IoT, smart sensors, and social media, to detect concept drifts, distinguish between the recurrent and non-recurrent traffic events, and  ...  from the heterogeneous data sources, and volatility of traffic conditions.  ...  Fig. 5 is a high-level illustration of the process. Once an event is identified by the concept drift detection, the Twitter data stream is analyzed to collect tweets that are relevant to the event.  ... 
doi:10.1109/tits.2019.2924883 fatcat:enaochz3vverdhfeg2poazc6ai

Labelling Drifts in a Fault Detection System for Wind Turbine Maintenance [chapter]

Iñigo Martinez, Elisabeth Viles, Iñaki Cabrejas
2018 Studies in Computational Intelligence  
Such concept drift events are desired to be detected by means of statistical detectors and window-based approaches.  ...  This unpredictable statistical change in the measured variable is known as concept drift.  ...  Acknowledgements This research has been supported by NEM Solutions, a technology-based company focused that provides intelligent maintenance of complex systems to O&M businesses.  ... 
doi:10.1007/978-3-319-99626-4_13 fatcat:77ovqk34hvda5a3qnrrziqpxmi

Industry 4.0 towards Forestry 4.0: Fire Detection Use Case

Radhya Sahal, Saeed H. Alsamhi, John G. Breslin, Muhammad Intizar Ali
2021 Sensors  
Querying windowing is the heart of any stream-processing platform which splits infinite data stream into chunks of finite data to execute a query.  ...  Dynamic query window-based processing can reduce the reporting time in case of missing and delayed events caused by data drift.In this paper, we present a novel dynamic mechanism to recommend the optimal  ...  Event streams are potentially unbounded, infinite sequences of records and unpredictable that represent events or changes in real-time.  ... 
doi:10.3390/s21030694 pmid:33498450 fatcat:oboxgvvgmveh7oumseypmdt3xi

Classification of Evolving Stream Data Using Improved Ensemble Classifier

S Seema
2012 International Journal of Data Mining & Knowledge Management Process  
Data mining is a user-centric process that is used to extract useful patterns from large volumes of data.  ...  Handling data streams is a difficult task due to the variations in the data and the frequent occurrences of concept drifts.  ...  This method helps in handling unpredictable nature of data streams better.  ... 
doi:10.5121/ijdkp.2012.2401 fatcat:irh2iwrhd5hjzfwlmr64gp6bka

A survey on concept drift adaptation

João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia
2014 ACM Computing Surveys  
Assuming a general knowledge of supervised learning in this paper we characterize adaptive learning process, categorize existing strategies for handling concept drift, discuss the most representative,  ...  The survey aims at covering the different facets of concept drift in an integrated way to reflect on the existing scattered state-of-the-art.  ...  Thus, in Process Mining 5 [van der Aalst 2012; 2011], the area of research dealing with the different kinds of analyses of (business) processes by extracting information from event logs recorded by an  ... 
doi:10.1145/2523813 fatcat:hmocrqsq5rgnhe6udanangvija

Evolving Stream Classification using Change Detection

Ahmad Mustafa, Ahsanul Haque, Latifur Khan, Michael Baron, Bhavani Thuraisingham
2014 Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing  
However, this may lead to failure of capturing the concept drift immediately. We try to determine the chunk size dynamically by exploiting change point detection (CPD) techniques on stream data.  ...  Most of the currently available approaches to classify stream data instances divide the stream data into fixed size chunks to fit the data in memory and process the fixed size chunk one after another.  ...  and malicious events.  ... 
doi:10.4108/icst.collaboratecom.2014.257769 dblp:conf/colcom/MustafaHKBT14 fatcat:ij7mruexgfbrdcdeka2dj7k2ge

Open challenges for data stream mining research

Georg Krempl, Myra Spiliopoulou, Jerzy Stefanowski, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi
2014 SIGKDD Explorations  
processing.  ...  In this paper, we provide some pioneer analysis on Tumblr from a variety of aspects.  ...  on the challenges in stream mining.  ... 
doi:10.1145/2674026.2674028 fatcat:y3bozzeohveibgxb5wmiwfcogm

Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study

Mustafa Amir Faisal, Zeyar Aung, John R. Williams, Abel Sanchez
2015 IEEE Systems Journal  
In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an Intrusion Detection System (IDS), which is a second line of defense after the primary  ...  Index Terms-Smart grid, advanced metering infrastructure, intrusion detection system, data stream mining.  ...  Thus, we propose to explore a number of data stream mining algorithms to detect the anomalous events or attacks for all three types of IDSs.  ... 
doi:10.1109/jsyst.2013.2294120 fatcat:7bowkudbg5dvznexkzvoduf24m
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