Mining Shape Expressions From Positive Examples

Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 39; no. 11; pp. 3809 - 3820
Main Authors: Bartocci, Ezio, Deshmukh, Jyotirmoy, Gigler, Felix, Mateis, Cristinel, Nickovic, Dejan, Qin, Xin
Format: Journal Article
Language:English
Published: New York IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Clustering
Computational and artificial intelligence
computer science
computers and information processing
Cyber-physical systems
Data mining
Design automation
formal languages
Integrated circuits
Learning automata
learning systems
Noise measurement
pattern recognition
Sensors
Shape
Sine waves
Specification and description languages
Specifications
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2020.3012240