Batch to Real-Time: Incremental Data Collection & Analytics Platform

Real-time data collection and analytics is a desirable but challenging feature to provide in data-intensive software systems. To provide highly concurrent and efficient real-time analytics on streaming data at interactive speeds requires a well-designed software architecture that makes use of a carefully selected set of software frameworks. In this paper, we report on the design and implementation of the Incremental Data Collection & Analytics Platform (IDCAP). The IDCAP provides incremental data collection and indexing in real-time of social media data; support for real-time analytics at interactive speeds; highly concurrent batch data processing supported by a novel data model; and a front-end web client that allows an analyst to manage IDCAP resources, to monitor incoming data in real-time, and to provide an interface that allows incremental queries to be performed on top of large Twitter datasets

A European research roadmap for optimizing societal impact of big data on environment and energy efficiency

We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to allow stakeholders and the big data community to identify and meet big data challenges, and to proceed with a shared understanding of the societal impact, positive and negative externalities, and concrete problems worth investigating. It builds upon a case study focused on the impact of big data practices in the context of Earth Observation that reveals both positive and negative effects in the areas of economy, society and ethics, legal frameworks and political issues. The roadmap identifies European technical and non-technical priorities in research and innovation to be addressed in the upcoming five years in order to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

Generic Solution Architecture Design of Regulatory Technology (RegTech)

Regulatory Technology, or RegTech, uses new technology that assists the financial industry, such as FinTech and banks, in meeting regulatory compliance. RegTech automates various regulatory compliance activities that were previously manual, such as regulatory interpretation and regulatory reporting, amidst the challenges of the increasing volume of regulations and operational data. Some cutting-edge technologies discovered at RegTech include big data analytics, artificial intelligence, machine learning, robotic process automation, and cloud computing. Although very dominant in the financial industry, RegTech solutions have the potential to be applied in other regulated industries besides finance. Several studies have explored the potential for applying RegTech in industries other than finance, such as charitable organizations, real estate marketplace, pharmaceuticals, and healthcare. Therefore, this study aims to design a generic RegTech solution architecture so that it can be adopted and applied in various regulated industries achieve regulatory compliance more efficiently. Based on the evaluation results, the proposed architecture can be applied in an industrial environment other than financial to be considered generic. Furthermore, an evaluation of the comparison of regulatory compliance business processes without and by implementing RegTech can produce a time efficiency of 95.16%. These results show that RegTech solutions can achieve regulatory compliance more efficiently

Transformation of Robotics Education in the Era of Covid-19: Challenges and Opportunities

The COVID-19 pandemic has significantly impacted many aspects of our social and professional life. To this end, Higher Education institutions reacted rather vastly to this unpreceded situation although many issues have been reported in the international literature since the emergence of the first global lockdown. As we are now transitioning back to the ‘normality’, universities and businesses consider the so-called ‘blended’ or ‘hybrid’ model as a means of facilitating the transition phase. In view of this decision, several studies can be identified wherein blended learning scenarios are proposed and described. The present work constitutes such an effort. Precisely, while adjusting the lens to the didactic of Robotics courses, we propose a blended learning model via which the laboratory activities are performed without the physical presence of the students in the physical context. The aforementioned objective is attained under the aid of the Virtual Reality technology coupled with the Digital Twin model. We hope that the ideas presented in this manuscript will motivate and inspire more researchers, instructional designers, and educators to consider the adoption of such alternative instructional techniques to mitigate the shortcomings that the remote education setting brings and further to improve the overall learning experience

A Methodology for Economic Crisis Policy Analytics

The development and success of the ‘business analytics’ in the private sector, in combination with the growing availability of large quantities of useful data in government agencies, gives rise to the emergence of the ‘policy analytics’ in the public sector. However, though some knowledge has already been developed in this area, extensive research is required in order to increase our knowledge base concerning the exploitation of these exponentially increasing quantities of data available in government, in combination with data from private sector firms as well, using advanced analytical techniques (from various areas, such as machine learning, statistics, simulation, etc.), in order to provide substantial support for all stages of public policies in various important policy domains. This paper makes a contribution in this direction, by describing a methodology for policy analytics in the economic policy domain, concerning a highly important problem: the economic crises, which repeatedly occur in market-based economies being an inevitable trait of them. Our methodology aims at the identification of firm’s characteristics that affect positively or negatively their sensitivity to the economic crisis, which enables a deeper understanding of the kinds of firms that exhibit higher sensitivity to economic crisis (i.e. have more negative consequences) and provides a basis for the design of public policies for supporting such firms. It exploits existing data from various public sources (e.g. Ministries of Finance, Statistical Authorities), in combination with data from private sources (e.g. business information firms, consulting firms), from which firm-level crisis sensitivity models are estimated. Furthermore, an application of the proposed methodology is presented, using data from Greek firms for the crisis period 2009 – 2014, which provides interesting insights

Big data analytics on container-orchestrated systems

Container-orchestration systems offer new possibilites to software architects seeking to make their software systems more scalable and reliable. In the past, these systems have been used to implement transactional software systems but, more recently, they have been applied to other areas including big data analytics. To understand the advantages and limitations such systems impose on software architects, I migrated an existing big data analytics infrastructure from a software architecture that required lots of work from its developers to deploy and maintain to the new software architecture provided by container-orchestration systems. My results show that scalability is increased, maintenance costs are reduced, and reliability is easier to achieve