Analytics Everywhere is our proposed conceptual framework that is developed based on edge-fog-cloud continuum to handle an enormous volume of incoming data streams from IoT devices and perform a network of analytical tasks (different analytical capabilities such as descriptive, diagnostic, and predictive analytics) according to a data life-cycle. This major breakthrough framework provided us with an iterative learning experience on how to advance our research towards automated analytical tasks for the Cyber Physical Systems/Internet of Things.
Our Analytics Everywhere research laboratory is located within the Faculty of Computer Science, at University of New Brunswick in Fredericton, NB, Canada. UNB is the oldest English-language university in Canada, and among the oldest public universities in North America with a tradition of more than 230 years. UNB aims to be a university of influence through excellence and innovation in research and teaching to enable positive social change across our communities. To fullfil this vision, our lab focus on tackling scientific challenges, engaging with our academic and industry partners, and inspring our students to to become problem solvers and leaders in the world.
Exploring the practical challenges and connect them with the theoretical research gaps as well as undertaking instensive research that addresses societal and scientific challenges.
Collaborating with our partners to build prototypes for solving practical problems and transferring our research outcomes to Canadian businesses to strengthen their national and global competitive.
Extending the academic freedom and fostering our student's passion for discovering; preparing HQP for the evolving job market and connecting industrial needs to our research and teaching programs.
Research & Development
Promoting the advances of Analytics Everwhere Framework in performing automated analytical tasks capable of providing higher-level intelligence from continuous IoT/CPS data streams and generating long-term insights from accumulated IoT/CPS data streams. Exploring the possibility to adapt our framework in building prototype to support multiple Social Good use cases.
Developing and evaluating scalable and adaptive Data Analytics Platforms for the IoT/CPS. Proposing unique solution to capture, manage, process, analyze, and visualize data streams through streaming descriptive, diagnostic, and predictive analytics. Incooprating human-in-the-loop, facilitating the interaction between human and future IoT/CPS platform to deploy in different Smart City scenarios.
IoT/CPS for combatting Climate Change
Empowering current efforts to combat climate change by leveraging the most advanced AI/ML techniques to analyze the large scale of these spatial-temporal data. Solving technical challenges in connectivity, communications and protocols of IoT/CPS platforms and applying new technologies in software, analytics, architecture configurations and platform infrastructure to address Climate Change issues.