An Adaptive Modeling Framework for Bivariate Data Streams with Applications to Change Detection in Cyber-Physical Systems

Plasse, Joshua; Noble, Jordan; Myers, Kary

doi:10.1109/icdmw.2017.151

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…The intuition behind the multinomial change detection method (MCDM) relies on the behavior of the adaptive (p t ) and static (p t ) estimates during periods of stationarity and drift. The idea of comparing the behavior of adaptive and static estimates is an extension of the work in Plasse et al (2017), where a similar concept was developed for detecting…”

Section: A Multinomial Change Detection Methodsmentioning

confidence: 99%

Multiple changepoint detection in categorical data streams

Plasse

Adams

2019

Stat Comput

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The need for efficient tools is pressing in the era of big data, particularly in streaming data applications. As data streams are ubiquitous, the ability to accurately detect multiple changepoints, without affecting the continuous flow of data, is an important issue. Change detection for categorical data streams is understudied, and existing work commonly introduces fixed control parameters while providing little insight into how they may be chosen. This is ill-suited to the streaming paradigm, motivating the need for an approach that introduces few parameters which may be set without requiring any prior knowledge of the stream. This paper introduces such a method, which can accurately detect changepoints in categorical data streams with fixed storage and computational requirements. The detector relies on the ability to adaptively monitor the category probabilities of a multinomial distribution, where temporal adaptivity is introduced using forgetting factors. A novel adaptive threshold is also developed which can be computed given a desired false positive rate. This method is then compared to sequential and nonsequential change detectors in a large simulation study which verifies the usefulness of our approach. A real data set consisting of nearly 40 million events from a computer network is also investigated.

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Section: A Multinomial Change Detection Methodsmentioning

confidence: 99%

Multiple changepoint detection in categorical data streams

Plasse

Adams

2019

Stat Comput

Self Cite

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“…• Other: Studies, which do not fit any of the above categories, are grouped under this category. They consider Business processes [17], workflows (process) [129], data [117].…”

Section: Addressed Cps Componentsmentioning

confidence: 99%

A Systematic Literature Review on Model-driven Engineering for Cyber-Physical Systems

Mohamed¹,

Kardaş²,

Challenger³

2021

Preprint

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This technical report presents a Systematic Literature Review (SLR) study that focuses on identifying and classifying the recent research practices pertaining to CPS development through MDE approaches. The study evaluates 140 research papers published during 2010-2018. Accordingly, a comprehensive analysis of various MDE approaches used in the development life-cycle of CPS is presented. Furthermore, the study identifies the research gaps and areas that need more investigation. The contribution helps researchers and practitioners to get an overall understanding of the research trends and existing challenges for further research/development.

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