Smart Data Management Research Articles

Smart Data Management: Enhancing Efficiency and Security in the Digital Age

Effective data management is essential for firms looking to use their data assets for strategic advantage in today's data driven environment. By using cutting edge technologies and practices, smart data management goes above and beyond conventional techniques to improve data security, quality, and accessibility. The concepts, advantages, and tactics of intelligent data management are examined in this article along with how it may turn data into a useful asset for a business

Learning from Our Mistakes?

Decisions based on artificial intelligence could end up being flawed because AI is being taught using biased data. While machine learning algorithms have the potential to enable greater safety, better access to mobility and more effective traffic management, data bias can lead to negative consequences, such as discrimination, unfairness and unreliability of data. We ask the experts how we can avoid unintended consequences in smart data management

Towards AI-driven transformation and smart data management: Emerging technological change in the public sector value chain

Governments worldwide are beginning to implement AI-based services to exploit data and seek solutions that provide value to citizens and assist in decision-making. AI-driven transformation and smart data management can replace the workforce or enhance it. This paper focuses on exploring AI-driven transformation and smart data management in the public sector value chain. Guided by two research questions, a systematic literature review was conducted using the PRISMA approach, complemented with empirical evidence on the emerging technological change applied to management levels and decision-makers in the public sector. The needs related to AI-driven transformation and smart data management for the public sector are characterized by an operational transformation, which includes human resources and know-how as the spearhead. Challenges have to do with the generation of efficient and transparent services that provide public value and promote the benefit of society. Some implications are related to governments deciding to plan digitalization that allows them to take advantage of the emerging technological change to improve activities along the value chain.

Digital Twins in Smart Data Management at a Manufacturing Enterprise

Digital Twins in Smart Data Management at a Manufacturing Enterprise

Use of UIoT for Offshore Surveys Through Autonomous Vehicles

Abstract The ENDURUNS project is a European Research project of the Horizon 2020 framework, which has as its main objective to achieve the optimum and intelligent use of green hydrogen energy for long-term ocean surveys. The ENDURUNS system comprises an Unmanned Surface Vehicle (USV) and an Autonomous Underwater Vehicle (AUV) with gliding capability. The power pack of the USV integrates Li-ion batteries with photovoltaic panels, whilst the AUV employs Li-ion batteries and a hydrogen fuel cell. It is essential to develop a continuous monitoring ca-pability for the different systems of the vehicles. Data transmission between the devices onboard presents challenges due to the volume and structure of the different datasets. A telecommunications network has been designed to manage the operational components considered in the project. The autonomous vehicles perform measurements, providing their position and other data wirelessly. The system will generate a great volume of various signals during the survey. The Remote Control Centre needs to be interfaced with the vehicles in order to receive, manage and store the acquired data. An Underwater Internet of Things (IoT) platform is designed to establish efficient and smart data management. This study presents an exhaustive survey to analyse the telecommunication systems employed in the autonomous vehicles, including the back-end, user interface and mobile units. This paper presents the novel design of the hardware and software structure of the ENDURUNS project with regard to the literature, where its components and their in-terconnection layers are detailed, which is a novel scientific and technological approach for autonomous seabed surveying in deep oceans or in coastal areas.

Smart Data as a Service

Nowadays, smart data emerge as a new research direction to create value from business data in an intelligent way. Smart data are defined as the data gathered and processed that can be used to create new insights for smart solutions to support business strategies. This paper aims at proposing a conceptual model for smart data management. In other words, the model can be used for designing a smart service system based on the perspective of service science that can manage and deliver smart data as a service.

The urban data deluge: challenges for smart urban planning in the third data revolution

ABSTRACT Effective urban planning is increasingly affected and governed by the current information society. This paper argues that the so-called third data revolution creates unprecedented challenges for sustainable city policy in the digital age. Three types of data revolutions are distinguished in this paper. The nowadays popular notion of smart cities may be helpful to enhance the cognitive quality of urban policy-making, but leads also to new issues on complex data handling in the modern age of social media and digital information systems. The various issues at stake are outlined in this paper, while this study is concluded with a sketch of the contours of a so-called smart city policy fly-wheel, so as to demonstrate that the urban fabric – also in a smart city context – is always work in progress, with a view to the achievement of sustainable development goals. Highlights The birth of the smart cities concept originates from the third data revolution. Sustainable city policy calls for smart data management. In the ‘New Urban World’ systematic data warehousing is needed. The tension between closed administrative city borders and open data systems can be bridged. The attainment of sustainable development goals (SDGs) presupposes smart city policy.

Regulatory roles and functions in information-based regulation: a systematic review

Information-based regulation occurs when regulators use information to drive changes in behaviours in order to achieve public policy objectives. Information-based regulation has emerged as an alternative way to regulate firms compared with more traditional direct command-and-control and market-based policy instruments within the contemporary regulatory state. Despite growing international interest, challenges remain in understanding the roles for regulators in information-based regulation, the functions of regulators in shaping and leveraging information flows, and the administrative capacities required to fulfil them. Based on a systematic review methodology, this article synthesises the findings of 130 peer-reviewed articles in the environmental, energy and food policy areas. It develops a typology of functions for regulators and outlines the new administrative capacities required in the contemporary regulatory state, particularly in standard setting, assurance and intermediation, and smart data management.Points for practitionersRegulation by information is becoming popular in many part of the world beyond its original genesis in the US and other developed countries. The design and implementation of such schemes creates new challenges for regulators. Our review integrates relevant research in three policy areas (environment, food and energy) and develops a new typology of functions performed by regulators. The article is the first to discuss how the roles and functions of regulators need to change in the contemporary information and regulatory environment. It also emphasises the importance of regulatory involvement in information-based regulation, which has traditionally been seen as a deregulatory approach.

A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems

Industry 4.0 represents the fourth phase of industry and manufacturing revolution, unique in that it provides Internet-connected smart systems, including automated factories, organizations, development on demand, and ‘just-in-time’ development. Industry 4.0 includes the integration of cyber-physical systems (CPSs), Internet of Things (IoT), cloud and fog computing paradigms for developing smart systems, smart homes, and smart cities. Given Industry 4.0 is comprised sensor fields, actuators, fog and cloud processing paradigms, and network systems, designing a secure architecture faces two major challenges: handling heterogeneous sources at scale and maintaining security over a large, disparate, data-driven system that interacts with the physical environment. This paper addresses these challenges by proposing a new threat intelligence scheme that models the dynamic interactions of industry 4.0 components including physical and network systems. The scheme consists of two components: a smart management module and a threat intelligence module. The smart data management module handles heterogeneous data sources, one of the foundational requirements for interacting with an Industry 4.0 system. This includes data to and from sensors, actuators, in addition to other forms of network traffic. The proposed threat intelligence technique is designed based on beta mixture-hidden Markov models (MHMMs) for discovering anomalous activities against both physical and network systems. The scheme is evaluated on two well-known datasets: the CPS dataset of sensors and actuators and the UNSW-NB15 dataset of network traffic. The results reveal that the proposed technique outperforms five peer mechanisms, suggesting its effectiveness as a viable deployment methodology in real-Industry 4.0 systems.