Big Data Processing Architecture Research Articles

Security and privacy controls for streaming data in extended intelligent environments

Intelligent environments collect and process personal information to assist individuals with their daily activities, enhance their experiences and adapt to their needs and intentions. The prosperity of paradigms like the Internet of Things (IoT) will boost the development of intelligent environments, but the envisioned exponential data growth will give rise to serious security and privacy concerns. Already in the domains of smart homes and healthcare one can observe a growing trend of intelligent environments being extended with third party smart service and technology providers - such as cloud and Big Data analytics services - that analyze and visualize sensitive information as a means to offer new insights to their customers, but that typically cross the personal space or privacy boundaries of the intelligent environment. The challenge addressed in this work is how to offer Big Data processing capabilities as a service with appropriate data protection safeguards in order to protect the individual's privacy in the extended intelligent environment. In this paper, we present SparkXS, a framework which offers granular and scalable access and data protection control on streaming data that can deal with the growing velocity, volume and variety of volatile data of IoT, integrated on top of our SAMURAI lambda architecture for Big Data processing. Driven by upcoming legislation and obligations, such as the EU General Data Protection Regulation (GDPR), our framework applies Privacy by Design (PbD) strategies and offers security controls that empower users to better control their personal data. Experimental results with motivating use cases and large data sets demonstrate the feasibility and scalability of our SparkXS framework while operating with acceptable performance overheads.

Multilayer Big Data Architecture for Remote Sensing in Eolic Parks

Due to their nature, Eolic parks are situated in zones with difficult access. As a result, management of Eolic parks using remote sensing techniques is of great importance. In addition, the huge amount of data managed by Eolic parks, together with their nature (distributed, heterogeneous, produced, consumed at different times, etc.) makes them ideal to apply big data techniques. In this paper, we present a multilayer hardware/software architecture that applies cloud computing techniques for managing big data from Eolic parks. This architecture allows tackling the processing of large, distributed, and heterogeneous data sets in a remote sensing context. An innovative contribution of this work is the combination of different techniques at three different layers of the proposed hardware/software architecture for Eolic park big data management and processing.