- Open Access
- Article
HivePool: An Exploratory Visualization System for Honey Beehive Data
by Tinghao Feng 
, Sophie Columbia , Christopher Campell and Rahman Tashakkori
Computer Science Department, Appalachian State University, Boone, 28608, USA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 9, Page # 27-60, 2024; DOI: 10.55708/js0309004
Keywords: Time Series Data Analysis, Event Visualization, Honey Beehive Data
Received: 20 August 2024, Revised: 12 September 2024, Accepted: 13 September 2024, Published Online: 26 September 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Feng, T., Columbia, S., Campell, C., & Tashakkori, R. (2024). HivePool: An exploratory visualization system for honey beehive data. Journal of Engineering Research and Sciences, 3(9), 61-74. https://doi.org/10.55708/js0309004
Chicago/Turabian Style
Feng, Tinghao, Sophie Columbia, Christopher Campell, and Rahman Tashakkori. “HivePool: An Exploratory Visualization System for Honey Beehive Data.” Journal of Engineering Research and Sciences 3, no. 9 (2024): 61-74. https://doi.org/10.55708/js0309004.
IEEE Style
T. Feng, S. Columbia, C. Campell, and R. Tashakkori, “HivePool: An Exploratory Visualization System for Honey Beehive Data,” Journal of Engineering Research and Sciences, vol. 3, no. 9, pp. 61-74, 2024, doi: 10.55708/js0309004.
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Abstract
Honey bee health is crucial for global ecosystems, but traditional data analysis methods often struggle to capture the complex interplay between bee behavior and environmental factors. To bridge this gap, we developed HivePool, a novel data visualization and analysis tool designed to empower beekeepers and researchers with deeper insights into these interactions. This paper explores HivePool’s functionalities, focusing on its interactive visualizations and innovative time-oriented pattern recognition for event prediction. By leveraging time series visualization techniques, HivePool allows users to explore not only static relationships between environmental variables but also how these variables change dynamically leading up to specific events within the hive. The paper showcases HivePool’s effectiveness through two use cases: data-driven event exploration and example-driven event prediction. Overall, HivePool equips beekeepers and researchers with a powerful set of tools, facilitating a deeper understanding of bee behavior and environmental influences, ultimately leading to
improved beehive health and management strategies.
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