Theses and Dissertations
ORCID
https://orcid.org/0000-0002-5230-8723
Issuing Body
Mississippi State University
Advisor
Rahimi, Shahram
Committee Member
Perkins, Andy
Committee Member
Mittal, Sudip
Committee Member
Chen, Zhiqian
Committee Member
Seale, Maria
Date of Degree
12-8-2023
Original embargo terms
Embargo 1 Year
Document Type
Dissertation - Open Access
Major
Computer Science
Degree Name
Doctor of Philosophy (Ph.D)
College
James Worth Bagley College of Engineering
Department
Department of Computer Science and Engineering
Abstract
The availability of a scalable and explainable rule extraction technique via motif discovery is crucial for identifying the health states of a system. Such a technique can enable the creation of a repository of normal and abnormal states of the system and identify the system’s state as we receive data. In complex systems such as ECG, each activity session can consist of a long sequence of motifs that form different global structures. As a result, applying machine learning algorithms without first identifying the local patterns is not feasible and would result in low performance. Thus, extracting unique local motifs and establishing a database of prototypes or signatures is a crucial first step in analyzing long temporal data that reduces the computational cost and overcomes imbalanced data. The present research aims to streamline the extraction of motifs and add explainability to their analysis by identifying their differences. We have developed a novel framework for unsupervised motif extraction. We also offer a robust algorithm to identify unique motifs and their signatures, coupled with a proper distance metric to compare the signatures of partially similar motifs. Defining such distance metrics allows us to assign a degree of semblance between two motifs that may have different lengths or contain noise. We have tested our framework against five different datasets and observed excellent results, including extraction of motifs from 100 million samples in 8.02 seconds, 99.90% accuracy in self-supervised ECG data classification, and an average error of 16.66% in RUL prediction of bearing failure.
Sponsorship
Engineer Research and Development Center (ERDC)
Recommended Citation
Bakhtiari Ramezani, Somayeh, "Scalable and explainable self-supervised motif discovery in temporal data" (2023). Theses and Dissertations. 6001.
https://scholarsjunction.msstate.edu/td/6001
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Numerical Analysis and Scientific Computing Commons