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.

Edge computing constitutes a promising paradigm of managing and processing the massive amounts of data generated by Internet of Things (IoT) devices. Data and computation are moved closer to the client, thus enabling latency- and bandwidth-sensitive applications. However, the distributed and heterogeneous nature of the edge as well as its limited resource capabilities pose several challenges in implementing or choosing an efficient edge-enabled storage system. Therefore, it is imperative for the research community to contribute to the clarification of the purposes and highlight the advantages and disadvantages of various edge-enabled storage systems. This work aspires to contribute toward this direction by presenting a performance analysis of three different storage systems, namely MinIO, BigchainDB, and the IPFS. We selected these three systems as they have been proven to be valid candidates for edge computing infrastructures. In addition, as the three evaluated systems belong to different types of storage, we evaluated a wide range of storage systems, increasing the variability of the results. The performance evaluation is performed using a set of resource utilization and Quality of Service (QoS) metrics. Each storage system is deployed and installed on a Raspberry Pi (small single-board computers), which serves as an edge device, able to optimize the overall efficiency with minimum power and minimum cost. The experimental results revealed that MinIO has the best overall performance regarding query response times, RAM consumption, disk IO time, and transaction rate. The results presented in this paper are intended for researchers in the field of edge computing and database systems.

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Data handling and provisioning play a dominant role in the structure of modern cloud–fog-based architectures. Without a strict, fast, and deterministic method of exchanging data we cannot be sure about the performance and efficiency of transactions and applications. In the present work we propose an architecture for a Data as a Service (DaaS) Marketplace, hosted exclusively in a cloud environment. The architecture includes a storage management engine that ensures the Quality of Service (QoS) requirements, a monitoring component that enables real time decisions about the resources used, and a resolution engine that provides semantic data discovery and ranking based on user queries. We show that the proposed system outperforms the classic ElasticSearch queries in data discovery use cases, providing more accurate results. Furthermore, the semantic enhancement of the process adds extra results which extend the user query with a more abstract definition to each notion. Finally, we show that the real-time scaling, provided by the data storage manager component, limits QoS requirements by decreasing the latency of the read and write data requests.