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.
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Techniques
VEKG
Video Event Knowledge Graph to Represent Video Streams for Complex Event Pattern Matching
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VIDCEP
Complex Event Processing Framework to Detect Spatiotemporal Patterns through user defined queries.
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Multimedia Pub/Sub
High performance Multimedia Event Processing based on the deep convolutional neural network (DNN) techniques
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Online Training
Supporting queries of seen as well as unseen subscriptions of multimedia events
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Microservices Architecture
Appropriate use of microservices for independent deployablility
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Features
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.
Containerisation
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.
Monitoring
GNOSIS uses Jaeger to trace and troubleshoot problems during processing.
Multimodal Processing
GNOSIS processes Multimodal Event Knowledge Graphs to provide a knowledge representation for multimodal event streams’ semantic, spatial, and temporal content.
News
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 moreResearch 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 moreAbout Us
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
Research Leader
Dr Amin Anjomshoaa
Research Fellow
Muhammad Jaleed Khan
PhD Researcher
Felipe Arruda Pontes
MSc Researcher
Dhaval Salwala
Research Assistant
Dr Piyush Yadav
Postdoctoral Researcher
Tarek Zaarour
PhD Researcher
Atiya Usmani
PhD Researcher
Niki Pavlopoulou
PhD Researcher
Praneet Dhingra
Research Assistant