- On Unified Stream Reasoning
- Aalborg Øst
The real-time integration of huge volumes of dynamic data from heterogeneous sources is getting more and more attention, as the number of data-stream sources is keeping growing and changing at very high pace. While Data Stream and Event Processing deal with data streams and reactiveness, Reasoning is a potential solution for the data heterogeneity: ontologies enable access to data streams from different sources and make explicit hidden information. Stream Reasoning aims at bringing together those areas, with techniques to perform reasoning tasks over data streams.
In this context, the problem I investigate is how to unify the current Stream Reasoning techniques, as they may substantially differ from each other. This fact is evident when these techniques are designed to reach different goals, e.g. aggregating data in the stream vs. detecting events. However, it happens even when they perform the same task and final users may expect the same behaviour. Understanding peculiarities and common points is mandatory in order to compare, contrast and integrate them.
The main outcome of this research is RSEP-QL, a formal model to describe the evaluation semantics of stream reasoning systems in the context of continuous query answering. RSEP-QL extends SPARQL by adding operators to manage streams such as sliding windows and event patterns. Similarly to SPARQL, RSEP-QL works under entailment regimes, which introduce deductive inference in the continuous query answering process. The value of RSEP-QL is shown through two applications in the areas of comparative testing and query optimization.
Speaker: Daniele Dell’Aglio
Daniele Dell’Aglio is a Research Fellow at University of Aberdeen and a PhD student at Politecnico di Milano. He is advised by Prof. Emanuele Della Valle, and his research activity focuses on Stream Reasoning, i.e. the application of inference techniques to data streams. In his major research topic, Daniele studies the problem of unifying Stream Reasoning techniques in the context of continuous query answering. Daniele won an IBM PhD Fellowship award 2014, and he is currently involved in the activities of the W3C Community Group on RDF Stream Processing. From 2008 to 2012, Daniele worked as junior researcher and consultant at CEFRIEL. He participated in the Smart City research activities of the LarKC FP7 project and in research activities related to Web services and recommender systems in the SOA4All and the Service Finder FP7 projects. Daniele holds a MSc and a BSc in computing system engineering (Politecnico di Milano). He contributed in the realization of several prototypes of services in the urban context, such as BOTTARI (1st prize at the Semantic Web challenge 2011), Traffic LarKC (1st prize at the AI Mashup challenge 2011), Twindex and ECSTASYS (respectively 3rd prize at the AI Mashup challenge 2013 and 2014).
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