scholarly journals Data Science and Analytics: An Overview from Data-Driven Smart Computing, Decision-Making and Applications Perspective

2021 ◽  
Author(s):  
Iqbal H. Sarker
Keyword(s):  
Decision Making ◽  
Real World ◽  
Data Science ◽  
Industrial Revolution ◽  
Point Of View ◽  
Data Driven ◽  
Digital World ◽  
Solution Point ◽  
Advanced Analytics ◽  
Smart Computing

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

scholarly journals Data-driven graph drawing techniques with applications for conveyor systems

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Simone Göttlich ◽  
Sven Spieckermann ◽  
Stephan Stauber ◽  
Andrea Storck
Keyword(s):  
Real World ◽  
Connected Graph ◽  
Stress Function ◽  
Graph Drawing ◽  
Point Of View ◽  
Data Driven ◽  
Challenging Problem ◽  
Conveyor System ◽  
System Graph ◽  
Real World Problems

AbstractThe visualization of conveyor systems in the sense of a connected graph is a challenging problem. Starting from communication data provided by the IT system, graph drawing techniques are applied to generate an appealing layout of the conveyor system. From a mathematical point of view, the key idea is to use the concept of stress majorization to minimize a stress function over the positions of the nodes in the graph. Different to the already existing literature, we have to take care of special features inspired by the real-world problems.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

scholarly journals A14D

Federalism-E ◽  
2020 ◽  
Vol 21 (2) ◽  
pp. 68-79
Author(s):  
Mayowa Oluwasanmi
Keyword(s):  
Artificial Intelligence ◽  
Human Rights ◽  
International Development ◽  
Industrial Revolution ◽  
Institutional Capacity ◽  
Data Driven ◽  
Democratic Process ◽  
Fourth Industrial Revolution ◽  
Digital World ◽  
Economic Dependency

In the forefront of the fourth industrial revolution is Artificial intelligence, better known as “AI.”  As a frontier technology, AI is implementing deep and far-reaching changes into the way we work, play and live. These tools present numerous opportunities in solving issues of international development. Yet in spite of its infallible potential,  the negative repercussions of AI driven change have become abundantly clear. These consequences will only be exacerbated in the Global South where there is a greater tendency for weak institutional capacity and governance. AI has the potential to threaten employment, human rights, democratic process and worsen economic dependency. The very nature of these tools--the ability to codify and reproduce patterns--must be met with responsible, ethical actors who ensure developmental goals will be met. Is AI4D the answer? This paper will illustrate the opportunities and risks of AI-driven development. I argue that technology can no longer be considered an inherent equalizer, and that the responsibility for fairness in the digital world must be championed by the international community. Finally, I will present possible steps policymakers can take to ensure true development in our data-driven future. 

scholarly journals Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

2022 ◽  
Vol 2022 ◽  
pp. 1-48
Author(s):  
Michael Yit Lin Chew ◽  
Ke Yan
Keyword(s):  
Fault Detection ◽  
Real World ◽  
Data Science ◽  
Building Design ◽  
Facility Management ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Main Concern ◽  
Smart Building ◽  
Detection And Diagnosis

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

To What Extent Should We Enrich Law and Economics? On Calabresi and his Future of Law and Economics

Global Jurist ◽  
2019 ◽  
Vol 19 (3) ◽  
Author(s):  
Régis Lanneau
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Real World ◽  
Law And Economics ◽  
Point Of View ◽  
The Future ◽  
Epistemological Point ◽  
Laws And Regulations

Abstract In this paper, I argue that the “expanded” economic theory advocated in Calabresi’s book “The Future of Law and Economics” could be interpreted in at least three different ways, all of which are compatible. First, Calabresi’s book could be interpreted as an attempt to incentivize lawyer-economists to explore laws and regulations from different angles or perspectives rather than merely apply neoclassical theories. Second, it could be considered an attempt to justify the introduction of the notion of moral costs into law and economics to better explain some legal realities. Third, it could be considered an attempt to advocate, in a more normative way, the need to incorporate moral costs into real world analysis to better improve upon decision making. This paper will address and discuss each of these possible interpretations. It will be clear that, from an epistemological point of view, if the first interpretation might be more widely accepted because it is less controversial, the second and third interpretations remain more problematic. Admittedly, the concept of moral costs could obscure and even distort our understanding of some legal realities. Moreover, the introduction of such costs for decision making is raising questions which cannot be answered through economic theory alone.

Sign in / Sign up

Export Citation Format

Share Document