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| subtitle | Reasoning upon rapidly changing information flows. | ||||
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More and more applications require real-time processing of data streams in oil&gas operations, in weather monitoring, in customer relationship management, in Smart Cities and in Social Media Analytics. For instance, in the last two domains, is public transportation where the people are? Who is driving the discussion about the top 10 emerging topics across all the social networks? A system able to answer those queries must:
- handle massive datasets,
- process data streams on the fly,
- cope with heterogeneous, incomplete and noisy data,
- provide reactive answers,
- support fine-grained information access, and
- integrate complex domain models.
Indeed, systems capable of tame the velocity dimension of Big Data exist. They can provide reactive fine-grained information access and analysis even in the presence of noisy data streams. Similarly, recent research on Data Integration and Semantic technologies – in particular on scalable Ontology Based Data Access (OBDA) – can tame the variety dimension of Big Data. OBDA can offer fine-grained information access to heterogeneous and incomplete datasets by reasoning on complex domain models so to rewrite ontological queries in SQL. However, none of those solutions can tame velocity and variety simultaneously especially when the information need is a complex data analysis. In 2008, he, Stefano Ceri, Frank van Harmelen and Dieter Fensel identified this challenge and formulate the Stream Reasoning research question [j3]:
is it possible to make sense in real time of multiple, heterogeneous, gigantic and inevitably noisy and incomplete data streams in order to support the decision process of extremely large numbers of concurrent users?
Since 2008, the Stream Reasoning research community conducted investigations and wrote papers that envision, elaborate, evaluate and discuss many aspects of this research question. The Stream Reasoning community document that a) the Semantic Web stack can be extended so to incorporate streaming data and events as a first class objects, b) the Stream Reasoning task is feasible, c) the very nature of streaming data offers opportunities to optimize reasoning, d) a combination of deductive and inductive stream reasoning techniques can cope with incomplete and noisy data. The mature Stream Reasoning solutions got deployed in real scenarios such as Smart City, Social Media Analytics, Oil & Gas, Energy, and Transport.