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Analytics Everywhere Lab
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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.

Recent News

  • June 16th 2022, Great news!!! Our article entitled "EVStationSIM: An End-to-End Platform to Identify and Interpret Similar Clustering Patterns of EV Charging Stations Across Multiple Time Slices" has been accepted to publish in Elsevier Applied Energy Journal (Impact Factor 9.746). Preprint of this paper can be found here.
  • February 21st 2022, Our article entitled "Discovering Self-Quantified Patterns using Multi-Time Window Models" has been accepted to publish in Applied Computing and Informatics (Q1 Journal).
  • October 28th 2021, Our book chapter entitled "A Spatial-temporal Comparison of EV Charging Station Clusters Leveraging Multiple Validity Indices" has been approved to publish in the Springer book series Communications in Computer and Information Science.
  • April 30th 2021, We won the Best Student Paper Award at the 10th International Conference on Smart Cities and Green ICT Systems. [Certification]
  • April 15th 2021, Our paper entilted "Edge-Cloud Intelligence in Self-Diagnostic of Land Mobile Radio Systems" has been accepted to publish in the 7th IEEE World Forum on the Internet of Things.
  • April 9th 2021, Our paper entilted "Prophet model for forecasting occupancy presence in indoor spaces using non-intrusive sensors" has been accepted to publish in the 24th AGILE Conference.
  • February 24th 2021, Our paper entilted "An Automated Clustering Process for Helping Practitioners to Identify Similar EV Charging Patterns Across Multiple Temporal Granularities" has been accepted to publish in the 10th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2021).

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Research & Development

At Analytics Everywhere Lab, we believe that Artificial General Intelligence might be only achieved through the ensemble of different task-specific AI agents in which an interconnected and self-governing network of AI solution agents collaborate to solve the growing complex problems. Keeping this in mind, we envision that the available Analytics Everywhere (AE) can incorporate an additional pillar for learning capability to achieve Learning and Intelligence Everywhere. Our research portfolio bridges the area of Cyber-Physical Systems (CPS), Internet of Things (IoT), Machine learning, Data Science, Embedded AI, Edge Computing, Cloud Computing, Context-enriched Analytics, Decision Intelligence, Hyper-Automation, and TinyML . Our research outcomes are being applied to solve numerous practical challenges. Specifically, we categorized our outcome into 4 main application groups.


Social Good

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.

Smart City

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.

Industry 4.0

Investigating and analyzing of Industrial IoT/CPS Ecosystems. Explaining the unknown and visualizing the decision making process by post-hoc analysis and build-in realtime detection mechanisms in our AE Framework. Understanding the tight relationship between the complexity of the algorithms and the data life-cycle to support the evolution of Industry 4.0.

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Our work is funded by
Contact us

Analytics Everywhere Lab

University of New Brunswick
Computer Science Faculty
Room GC107,
550 Windsor St, Fredericton,
Canada, E3B 5A3

Email: hcao3[at]unb[dot]ca

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