Web Stream Reasoning: From Data Streams to Actionable Knowledge

  • Alessandra MileoEmail author
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9203)


A fast growing torrent of data is being created by companies, social networks, mobile phones, smart homes, public transport vehicles, healthcare devices, and other modern infrastructures. Being able to unlock the potential hidden in this torrent of data would open unprecedented opportunities to improve our daily lives that were not possible before. Advances in the Internet of Things (IoT), Semantic Web and Linked Data research and standardization have already established formats and technologies for representing, sharing and re-using (dynamic) knowledge on the Web. However, transforming data into actionable knowledge requires to cater for (i) automatic mechanisms to discover and integrate heterogeneous data streams on the fly and extract patterns for applications to use, (ii) concepts and algorithms for context and quality-aware integration of semantic data streams, and (iii) the ability to synthesize domain-driven commonsense knowledge (and answers derived from it) with expressive inference that can capture decision analytics in a scalable way. In the first part of this lecture we will characterize the main approaches to stream processing for the Web of Data, showing how data quality and context can guide semantic integration. In the second part of this lecture we will focus on rule-based Web Stream Reasoning and illustrate how scalability and uncertainty issues can be addressed in a rule-based approach. We will discuss new challenges and opportunities in Web Stream Reasoning, briefly considering economical and societal impact in real application scenarios in a smart city context, and we will conclude by providing a brief overview of ongoing research and standardization activities in this area.


Stream reasoning Continuous query processing Quality of information Logic programming Semantic web Inductive logic reasoning 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Insight Centre for Data AnalyticsNational University of IrelandGalwayIreland

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