Multimodal Event Processing Engine
MEP provides a formal basis for native approaches to multimodal content analysis (e.g., computer vision, linguistics, and audio) within the event processing paradigm to support real-time queries over multimodal data streams. MEP enables the user to process multimodal streams under the event-processing paradigm and simplifies the querying of multimodal event streams.Learn More
Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern MatchingLearn More
Complex Event Processing Framework to Detect Spatiotemporal Patterns through user defined queries.Learn More
High performance Multimedia Event Processing based on the deep convolutional neural network (DNN) techniquesLearn More
Supporting queries of seen as well as unseen subscriptions of multimedia eventsLearn More
Appropriate use of microservices for independent deployablilityLearn More
Occupational Health and Safetylearn more
Smart Traffic Managementlearn more
Smart Agriculturelearn more
Activity Recognitionlearn more
Pose Detectionlearn more
Power of Graphs
Powered by Python’s graph library, GNOSIS creates complex network of detected objects to find complex patterns
Handle Multiple Video Streams
GNOSIS can process multiple video streams in near real-time with expressive reasoning over streams.
GNOSIS provides Docker container for quick and efficient deployment of the framework.
Suite of Video Query Operations
GNOSIS allows users to express high-level queries to process complex events in video streams.
GNOSIS uses Jaeger to trace and troubleshoot problems during processing.
GNOSIS processes Multimodal Event Knowledge Graphs to provide a knowledge representation for multimodal event streams’ semantic, spatial, and temporal content.
Demo for VLDB Endowment 2021
Yadav, Piyush, Dhaval Salwala, Felipe Arruda Pontes, Praneet Dhingra and Edward Curry (2021). “Query-Driven Video Event Processing for the Internet of Multimedia Things” Proceedings of the VLDB Endowment (VLDB) Demo[…]Read more
Research Study accepted in VLDB Endowment 2021
Yadav, Piyush, Dhaval Salwala, Felipe Arruda Pontes, Praneet Dhingra and Edward Curry (2021). “Query-DrivenVideo Event Processing for the Internet of Multimedia Things” Proceedings of the VLDB Endowment (VLDB) 14(12)Read more
The Open Distribued Systems (ODS) unit at the Insight Centre for Data Analytics, NUI Galway investigates novel techniques to contribute to decentralised and open distributed infrastructure. In collaboration with scientific and industrial partners the domain will identify and develop techniques to impacts people and organisation.
Areas of work: Event processing, Middleware, Smart cities, Incremental data management, Publish-subscribe
Dr Edward Curry
Dr Amin Anjomshoaa
Muhammad Jaleed Khan
Felipe Arruda Pontes
Dr Piyush Yadav