2025
Rahman, Yeaminur
Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards Masters Thesis
2025.
Abstract | Links | BibTeX | Tags: behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding
@mastersthesis{nokey,
title = {Adapting Eco-Driving Feedback and Historical Visualization for Vessel Dashboards},
author = {Yeaminur Rahman},
url = {https://hdl.handle.net/10222/85531},
year = {2025},
date = {2025-11-25},
urldate = {2025-11-25},
abstract = {Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.},
keywords = {behaviour change, climate, dashboard, geospatial analytics, mobile, navigation, peripheral vision, simulation, training, virtual environment, visualization, wayfinding},
pubstate = {published},
tppubtype = {mastersthesis}
}
Maritime navigation is a significant source of greenhouse gas emissions. While large-scale cargo shipping is the major contributor, smaller maritime operations, including patrolling, fishing, public transit, and recreation, present unique challenges and opportunities for power management. Fuel consumption, power conversion, and environmental data can permit environmentally conscious and cost-effective decision-making when driving a boat. To achieve this, we need to understand how best to integrate such data into boat dashboard interfaces. In this work, we design an Eco Dashboard inspired by eco-driving feedback dashboards in the automotive industry, as well as a variant of the Eco Dashboard that additionally visualizes historical route and fuel consumption data (Eco + Historical Dashboard). In an experimental simulation (N = 30) involving 12 experienced mariners and 18 novices, we compared both interfaces with a typical boat dashboard that presented fuel and speed. Our findings suggest that dashboards incorporating historical data, alongside eco-driving features, improve fuel efficiency and decision-making, particularly for non-experienced users. The Eco Dashboard supported real-time adjustments during complex navigation, whereas the Eco + Historical Dashboard enhanced route planning and confidence in longer-term decisions. Participants also reported greater confidence and reduced cognitive load when using these systems. These results provide valuable insights for the future design of maritime dashboard systems, offering a pathway to more effective and environmentally conscious navigation tools.
Amirkandeh, Melika Besharati
A Comparative Evaluation of Natural Language and Dashboard Interfaces for Visualizing Real-Time Monitoring Data Masters Thesis
2025.
Abstract | Links | BibTeX | Tags: chat interface, dashboard, visualization
@mastersthesis{nokey,
title = {A Comparative Evaluation of Natural Language and Dashboard Interfaces for Visualizing Real-Time Monitoring Data},
author = {Melika Besharati Amirkandeh},
url = {https://hdl.handle.net/10222/85441},
year = {2025},
date = {2025-09-23},
urldate = {2025-09-23},
abstract = {Natural Language Interfaces (NLIs) are emerging as an alternative to Dashboard Interfaces for data visualization, allowing users to formulate queries using conversational input rather than structured commands. In a controlled study (N=24) we compare an NLI-driven chatbot and a commercial visualization dashboard (RealFishPro) for a set of randomized analytics tasks involving real-time monitoring data. We find no difference in System Usability Scale (SUS) scores and no difference in task accuracy scores between interface conditions. NASA TLX scores show significantly higher mental and temporal demand when using the chatbot vs. the dashboard, and the chatbot yielded significantly higher task times overall. This pattern shows that while NLIs are flexible, they often impose greater cognitive effort and slower interaction compared to dashboards. Participant feedback indicated complementary strengths: NLIs were praised for simplicity and adaptability, dashboards for precision and clarity. These findings suggest that hybrid solutions integrating natural language and traditional interfaces could enhance data exploration and decision-making.},
keywords = {chat interface, dashboard, visualization},
pubstate = {published},
tppubtype = {mastersthesis}
}
Natural Language Interfaces (NLIs) are emerging as an alternative to Dashboard Interfaces for data visualization, allowing users to formulate queries using conversational input rather than structured commands. In a controlled study (N=24) we compare an NLI-driven chatbot and a commercial visualization dashboard (RealFishPro) for a set of randomized analytics tasks involving real-time monitoring data. We find no difference in System Usability Scale (SUS) scores and no difference in task accuracy scores between interface conditions. NASA TLX scores show significantly higher mental and temporal demand when using the chatbot vs. the dashboard, and the chatbot yielded significantly higher task times overall. This pattern shows that while NLIs are flexible, they often impose greater cognitive effort and slower interaction compared to dashboards. Participant feedback indicated complementary strengths: NLIs were praised for simplicity and adaptability, dashboards for precision and clarity. These findings suggest that hybrid solutions integrating natural language and traditional interfaces could enhance data exploration and decision-making.
2024
Ghaeinian, Seyed Adel
Design and Implementation of an Interactive Visual Querying System for Maritime Data Masters Thesis
Dalhousie University, 2024.
Abstract | BibTeX | Tags: AMNIS, tabletop displays, visualization
@mastersthesis{Ghaeinian2024,
title = {Design and Implementation of an Interactive Visual Querying System for Maritime Data},
author = {Seyed Adel Ghaeinian},
year = {2024},
date = {2024-06-10},
school = {Dalhousie University},
abstract = {Automatic Identification System (AIS) data is a crucial foundation of maritime operations such as navigation, traffic management, and safety monitoring. This requires robust management, visualization, and interactive visual analytics systems for the operators. Since usability, effectiveness, and accuracy are key factors in such maritime operations, it is important to design, implement, and evaluate these tools meticulously and incorporate the latest advancements in user interaction and analytics. This research explores the design and implementation of a novel system architecture for maritime data querying and exploration, enabling enhanced user interactions through direct-manipulation techniques. This system architecture provides a collaborative environment that incorporates Mixed Reality (MR), a touchable table-top interface, as well as iterative design and evaluation of a graph-based Visual Query Builder (VQB). The aim of developing the VQB interface is to allow non-experts to explore and query maritime data without the need of having technical skills while enhancing the decision-making process and spatial awareness of the maritime operations. This research further evaluates the VQB interface in terms of efficiency, accuracy, and user preferences by conducting a user study. For this study, we developed a baseline web-based textual interface for SPARQL queries, enhanced with auto-correction features and additional spatial querying capabilities, to effectively measure and fairly compare the performance of the VQB interface. 20 students from the faculty of computer science participated in this study without prior knowledge of RDF data querying and SPARQL language. Overall, the results of this study were promising as it showed a higher efficiency and accuracy rate as well as less perceived workload among participants in the VQB interface compared to the baseline. These findings highlight the role of visual query interfaces in improving the user experience as well as elevating efficiency, especially for non-experts, allowing them to explore and query maritime data without the need to learn technical skills.},
keywords = {AMNIS, tabletop displays, visualization},
pubstate = {published},
tppubtype = {mastersthesis}
}
Automatic Identification System (AIS) data is a crucial foundation of maritime operations such as navigation, traffic management, and safety monitoring. This requires robust management, visualization, and interactive visual analytics systems for the operators. Since usability, effectiveness, and accuracy are key factors in such maritime operations, it is important to design, implement, and evaluate these tools meticulously and incorporate the latest advancements in user interaction and analytics. This research explores the design and implementation of a novel system architecture for maritime data querying and exploration, enabling enhanced user interactions through direct-manipulation techniques. This system architecture provides a collaborative environment that incorporates Mixed Reality (MR), a touchable table-top interface, as well as iterative design and evaluation of a graph-based Visual Query Builder (VQB). The aim of developing the VQB interface is to allow non-experts to explore and query maritime data without the need of having technical skills while enhancing the decision-making process and spatial awareness of the maritime operations. This research further evaluates the VQB interface in terms of efficiency, accuracy, and user preferences by conducting a user study. For this study, we developed a baseline web-based textual interface for SPARQL queries, enhanced with auto-correction features and additional spatial querying capabilities, to effectively measure and fairly compare the performance of the VQB interface. 20 students from the faculty of computer science participated in this study without prior knowledge of RDF data querying and SPARQL language. Overall, the results of this study were promising as it showed a higher efficiency and accuracy rate as well as less perceived workload among participants in the VQB interface compared to the baseline. These findings highlight the role of visual query interfaces in improving the user experience as well as elevating efficiency, especially for non-experts, allowing them to explore and query maritime data without the need to learn technical skills.