Discovery of trend dependencies over time-series

We improve constraint-based data quality using trend dependency (TD) discovery, extending existing order dependencies (ODs) to allow variations and exceptions. Unlike ODs, TDs capture approximate functional mappings between attributes, addressing the limitations of monotonicity. Our approach involves automatic discovery over entire datasets and piecewise subsets. Optimizing across all possible mappings is impractical, but a single linear pass enables efficient pruning, making segmentation and trend discovery feasible with minimal accuracy loss. We conducted comprehensive experiments on real-world and synthetic data to evaluate our models.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Automotive Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Bode, Nicholas

Advisors dc:contributor.advisor

• Davoudi, Kourosh
• Szlichta, Jarek

• Data quality
• Data cleaning
• Quality constraints
• Segmented representation
• Symbolic regression

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1708

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1708