Autonomous Semantic Stream Processing

Autonomous Semantic Stream Processing
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This is a past event

Venue: Room 311, Meston Building

Stream data from physical and virtual worlds (sensory streams, social streams, etc.) is ubiquitous for modern data-intensive applications, e.g connected vehicles. Such applications collect and process information flows through a set of processors, each running on a different node of a computer network, which collaborate to perform the actual processing of information. This kind of network of processors provides a combinatorial explosion of strategies on coordinating and clustering the processing nodes with respect to probably conflicted optimization goals. Moreover, due to their continuous processing nature, the planning and optimizing operations have to adapt the constant redistribution of processing loads and communication topologies. This makes it extremely difficult to have a unified solution for coordinating such dynamic complex systems. In this light,  my talk will share the experience on my journey from building RDF Stream Processing engines to creating a platform for Autonomous Semantic Stream Processing, called ASAP. ASAP aims to deal with the problem of this kind with three features, Semantic Stream Fusion, Autonomous Processing Framework and Cooperative Optimization Mechanisms. Semantic Stream Fusion is a declarative programming environment for fusing heterogeneous stream data sources into a real-time knowledge graph. The Autonomous Processing Framework develops adaptive execution kernels which can autonomously coordinate with neighbours to process networked data flows. Via Cooperative Optimization Mechanisms, these autonomous execution kernels can cooperatively optimize towards game-theoric optimums, i.e equilibria instead of local traditional cost-models of data stream management systems.

Brief Bio

Danh Le Phuoc is a Marie SkÅ‚odowska-Curie Fellow at Technical University of Berlin(TUB). He  received  his PhD  in  Computer  Science  from  the  National  University  of Ireland.  He is working on Pervasive Analytics which includes Linked Data/Semantic Web, Pervasive Computing, Future Internet and Big Data for Internet of Everything. Before joining TUB, he was a Principle Investigator, Research Fellow and Project Lead of the Insight Centre of Data Analytics or Digital Enterprise Research Institute, at the National University of Ireland, Galway. He won various awards on building RDF-based data processing engines such as Semantic Web Pipes, Linked Sensor/Stream Middleware and Graph of Things. Currently, he is pushing the idea of Semantic Stream Processing for networked systems such as connected vehicles and real-time web of things via a radial overhaul his PhD work on building RDF Stream Processing engines. Relevant publications can be found in his Google Scholar profile at https://scholar.google.de/citations?user=7-k4HCoAAAAJ&hl=en. 

Speaker
Danh Le Phuoc
Hosted by
Jeff Pan
Venue
Meston Lecture Theatre 311