Ph.D. in Information Technology: Final Dissertation
DEIB - Conference Room
July 15th, 2016
2.30 pm
July 15th, 2016
2.30 pm
Abstract
On July 15th, 2016 the final dissertation of the candidate of the Ph.D. in Information Technology will be held at DEIB Conference Room and will start at 2.30 pm:
Daniele Dell'Aglio – XXVIII Cycle
"On Unified Stream Reasoning"
Supervisor: Prof. Emanuele Della Valle
Abstract:
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 an application in the area of comparative testing.
Daniele Dell'Aglio – XXVIII Cycle
"On Unified Stream Reasoning"
Supervisor: Prof. Emanuele Della Valle
Abstract:
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 an application in the area of comparative testing.
