Designing Traceability into Big Data Systems

Providing an appropriate level of accessibility and traceability to data or process elements (so-called Items) in large volumes of data, often Cloud-resident, is an essential requirement in the Big Data era. Enterprise-wide data systems need to be designed from the outset to support usage of such Items across the spectrum of business use rather than from any specific application view. The design philosophy advocated in this paper is to drive the design process using a so-called description-driven approach which enriches models with meta-data and description and focuses the design process on Item re-use, thereby promoting traceability. Details are given of the description-driven design of big data systems at CERN, in health informatics and in business process management. Evidence is presented that the approach leads to design simplicity and consequent ease of management thanks to loose typing and the adoption of a unified approach to Item management and usage.Comment: 10 pages; 6 figures in Proceedings of the 5th Annual International Conference on ICT: Big Data, Cloud and Security (ICT-BDCS 2015), Singapore July 2015. arXiv admin note: text overlap with arXiv:1402.5764, arXiv:1402.575

The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the existing communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model that can manage big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data

digitalization technologies for industrial sustainability

Abstract Digital technologies are shown to perform a potential role in developing a resource efficient industrial base. The effective adoption of them can help to deliver reduced costs and improve the flexibility and sustainability of manufacturing systems. However, these positive benefits are far from guaranteed and the way in which digital technologies favor the transition towards sustainable manufacturing systems has not been analyzed in detail yet, so more conceptual and empirical investigations are required in this field. This paper develops a conceptual framework, which explains the potential significance of using digital technologies toward efficiency, resilience and sustainability. It also includes evidence from various case studies, which illustrate the core technologies which can potentiality contribute to a sustainable industrial future. The findings show some impressive results concerning the sustainable implications of the digitalization of manufacturing processes. If the predicted benefits can be achieved through digital technologies, they could massively impact on sustainability

Digitalization technologies for industrial sustainability

Digital technologies are shown to perform a potential role in developing a resource efficient industrial base. The effective adoption of them can help to deliver reduced costs and improve the flexibility and sustainability of manufacturing systems. However, these positive benefits are far from guaranteed and the way in which digital technologies favor the transition towards sustainable manufacturing systems has not been analyzed in detail yet, so more conceptual and empirical investigations are required in this field. This paper develops a conceptual framework, which explains the potential significance of using digital technologies toward efficiency, resilience and sustainability. It also includes evidence from various case studies, which illustrate the core technologies which can potentiality contribute to a sustainable industrial future. The findings show some impressive results concerning the sustainable implications of the digitalization of manufacturing processes. If the predicted benefits can be achieved through digital technologies, they could massively impact on sustainability. \ua9 2019 The Authors. Published by Elsevier B.V

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Social networks research for sustainable smart education

Social networks research has grown exponentially over the past decade. Subsequent empirical and conceptual advances have been transposed in the field of education. As the debate on delivering better education for all gains momentum, the big question is how to integrate advances in social networks research, corresponding advances in information and communication technology (ICT) and effectively employ them in the domain of education. To address this question, this paper proposes a conceptual framework (maturity model) that integrates social network research, the debate on technology-enhanced learning (TEL) and the emerging concept of smart education

AI-based Smart Proxy Modelling of IBDP Field Reservoir Pressure of Carbon Sequestration

The application of artificial intelligence (AI) and machine learning (ML) in engineering holds great promise for addressing the challenge of climate change. One key focus in the fight against climate change is the geological storage of CO2, which has gained significant attention as a crucial strategy for mitigating greenhouse gas emissions. The primary objective is to ensure the safe and controlled containment of injected CO2 over an extended period, which has proven to be a major challenge in the journey toward carbon capture, and geologic storage (CCS). To achieve this goal, several critical tasks must be accomplished. These include ensuring the quality control of potential underground storage sites (candidate wells), monitoring the conditions of the CO2 saturation plume, and simulating the behavior of reservoir pressure distribution over time. Traditionally, the characterization of fluid flow in the oil and gas industry has heavily relied on numerical simulators, and this conventional approach remains the primary tool in CCUS for executing the tasks mentioned above. However, these numerical reservoir models are typically extensive, with tens of millions of grid blocks, and their potential remains largely untapped due to their high computational demands and time-consuming nature. Consequently, there is a pressing need for an efficient alternative tool that can facilitate swift and reliable decision-making processes. In this study an AI-based proxy model has been developed to replicate the pressure distribution of the injected CO2 as captured by the numerical simulation model (Eclipse) used to monitor the IBDP storage site. Employing Artificial Neural Networks (ANNs) and data-driven techniques, this study develops Smart Proxy models to reduce the high computational cost of reservoir simulation modeling. AI-based Smart proxy models are data-driven machine learning models that can accurately replicate the output of complex numerical reservoir simulation models at every layer and every time step in a fraction of the time (minutes). To develop a Smart Proxy Model, ANN algorithms are trained on large volumes of subsurface data to learn the complex patterns of fluid flow in a reservoir. 100 reservoir simulation realizations with varying geological properties, such as porosity, permeability, baffles and faults was provided for this project, with 2 injection wells; one active and the other inactive. The simulation results, including the geological reservoir properties of 15 of these realizations was selected, with developed features from the model was captured and used to train the Smart Proxy model. 10 realizations were left as blind validation dataset to perform final evaluation of the developed model. The results show that the developed Smart Proxy Model can successfully mimic the pressure distribution of the Eclipse outputs at every grid layer, and every time step of the model simulation