SKEL Lab travells to Canada for iASiS

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Dr. G.Paliouras travelled to Canada in September 2018, as an invited speaker for the second time in the frame of iASiS project. His first stop was in Toronto at the Fields Institute on Spetember 25th where he gave a talk about "Data integration and analysis for personalized medicine" whilst focusing on iASiS project progress.

http://www.fields.utoronto.ca/talks/Data-integration-and-analysis-person...

Abstract:
As biomedical data are made available in large quantities and variety, we face a unique opportunity and obligation: to use the data to accelerate the discovery of tailored treatments for individuals or small groups thereof. The ability to integrate and analyze heterogeneous biomedical data has so far proved to be a challenging undertaking. We are therefore in search for methods and tools that will address this urgent need. One effort in this direction is the research project iASiS, intermediate results of which I will present in this talk. iASiS proposes two levels of data analysis: first at the stage of homogenous datasets, such as medical images or clinical notes, and then at a level that combines insights from heterogeneous sources. In order to facilitate the latter type of analysis, the results of the former type are brought together in a large knowledge graph. In the talk, I will present the structure of the iASiS knowledge graph, the methods that are used for the different types of analysis, and examples of potentially interesting findings.

Next stop on the Canada tour was Dalhousie University were Dr.Paliouras gave a talk on "Complex Event Recognition for Maritime Monitoring". https://www.dal.ca/news/events/2018/09/27/complex_event_recognition_for_...

Abstract:

The aim of this talk is to present research results of the Complex Event Recognition lab (http://cer.iit.demokritos.gr/) of NCSR Demokritos (Athens, Greece), focusing on maritime applications. Maritime monitoring systems support safe shipping, through real-time detection of dangerous, suspicious and illegal vessel activities. We have been developing a complex event recognition system for maritime monitoring in the Event Calculus, allowing both for verification and real-time performance. The basic system is being developed through collaboration with domain experts, constructing effective patterns of maritime activity. In order to refine these patterns, we have developed online, relational learning techniques and applied them on AIS data streams. More recently, we have also been developing complex event forecasting techniques, allowing for predictive maritime analytics. In the talk, we will show results of our techniques on real AIS streams, covering large geographical areas.

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