Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people's everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper

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Abstract-Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The high expressiveness of non-monotonic reasoning enables complex decision making by managing defaults, commonsense, preferences, recursion, and non-determinism, but it is computationally intensive. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-of-the-art non-monotonic reasoners to be applied to streaming information in a scalable way.In this paper, we address the issue of scalability for nonmonotonic stream reasoning based on Answer Set Programming (ASP) -an expressive reasoning approach based on disjunctive logic Datalog with negation under the stable model semantics, by analyzing input dependency. We introduce an input dependency graph to represent the relationships between input events based on the structure of a given logical rule set. The input dependency graph allows us to dynamically configure the streaming window size in order to maximise the scalability of the non-monotonic reasoner. We conduct an experimental evaluation to demonstrate the effectiveness and ability of our proposed approach in improving the scalability of disjunctive logic programming with ASP in dynamic environments.

Stream reasoning is an emerging research area focused on providing continuous reasoning solutions for data streams. The exponential growth in the availability of streaming data on the Web has seriously hindered the applicability of state-ofthe-art expressive reasoners, limiting their applicability to process streaming information in a scalable way. In this scenario, in order to reduce the amount of data to reason upon at each iteration, we can leverage advances in continuous query processing over Semantic Web streams. Following this principle, in previous work we have combined semantic query processing and nonmonotonic reasoning over data streams in the StreamRule system. In the approach, we specifically focused on the scalability of a rule layer based on a fragment of Answer Set Programming (ASP). We recently expanded on this approach by designing an algorithm to analyze input dependency so as to enable parallel execution and combine the results. In this paper, we expand on this solution by providing i) a proof of correctness for the approach, ii) an extensive experimental evaluation for different levels of complexity of the input program, and iii) a clear characterization of all the algorithms involved in generating and splitting the graph and identifying heuristics for node duplication, as well as partitioning the reasoning process via input splitting and combining the results.