City Research Online

Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series

Andrienko, N. ORCID: 0000-0002-8574-6295, Andrienko, G. ORCID: 0000-0002-8574-6295, Artikis, A. , Mantenoglou, P. & Rinzivillo, S. (2024). Human-in-the-Loop: Visual Analytics for Building Models Recognising Behavioural Patterns in Time Series. IEEE Computer Graphics and Applications, doi: 10.1109/mcg.2024.3379851

Abstract

Results of automated detection of complex patterns in temporal data, such as trajectories of moving objects, may be not good enough due to the use of strict pattern specifications derived from imprecise domain concepts. To address this challenge, we propose a novel visual analytics approach that combines expert knowledge and automated pattern detection results to construct features that effectively distinguish patterns of interest from other types of behaviour. These features are then used to create interactive visualisations enabling a human analyst to generate labelled examples for building a feature-based pattern classifier. We evaluate our approach through a case study focused on detecting trawling activities in fishing vessel trajectories, demonstrating significant improvements in pattern recognition by leveraging domain knowledge and incorporating human reasoning and feedback. Our contribution is a novel framework that integrates human expertise and analytical reasoning with ML or AI techniques, advancing the field of data analytics.

Publication Type: Article
Additional Information: © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.”
Publisher Keywords: Trajectory, Data models, Pattern recognition, Time series analysis, Task analysis, Visual analytics, Training
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology
School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > giCentre
SWORD Depositor:
[thumbnail of Hybrid_human_machine_discovery_of_complex_movement_patterns__CGAA_.R1.pdf]
Preview
Text - Accepted Version
Download (4MB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login