Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications
This work addresses data management and analysis issues for smart city developers and researchers, but it appears incremental as it builds on existing SOA and data analytics methods.
The paper tackles the challenge of processing and analyzing large-scale IoT and social networking data for smart city applications, resulting in a platform that handles millions of user-generated events and reduces delays in data processing through algorithmic improvements.
In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big datasets stemming from social networking (SN) sites and Internet of Things (IoT) devices, collected by smart city applications and socially-aware data aggregation services. A large set of city applications in the areas of Participating Urbanism, Augmented Reality and Sound-Mapping throughout participating cities is being applied, resulting into produced sets of millions of user-generated events and online SN reports fed into the RADICAL platform. Moreover, we study the application of data analytics such as sentiment analysis to the combined IoT and SN data saved into an SQL database, further investigating algorithmic and configurations to minimize delays in dataset processing and results retrieval.