A Scalable and Semantic Data as a Service Marketplace for Enhancing Cloud-Based Applications

Evangelos Psomakelis, Anastasios Nikolakopoulos, Achilleas Marinakis, Alexandros Psychas, Vrettos Moulos, Theodora Varvarigou and Andreas Christou
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Evangelos Psomakelis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Anastasios Nikolakopoulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Achilleas Marinakis: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Alexandros Psychas: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Vrettos Moulos: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Theodora Varvarigou: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece
Andreas Christou: School of Electrical and Computer Engineering, National Technical University of Athens, 15780 Athens, Greece

Future Internet, 2020, vol. 12, issue 5, 1-21

Abstract: 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.

Keywords: cloud; fog; mongodb; daas; data as a service; performance analysis; qos ensurance; content discovery; qos monitoring (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
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