Critical Approaches To Data Engineering Systems And Analysis

The current data engineering demands more than theoretical understanding; it necessitates a practical, nuanced approach. Data engineering involves the intricate orchestration of systems and architectural frameworks for collecting, storing, processing, and analyzing vast datasets. The challenge lies in ensuring this data is managed and harnessed effectively, fostering insightful knowledge and steering organizations toward data-driven decision-making. Critical Approaches to Data Engineering Systems and Analysis unveils the latent potential inherent in diverse data analysis and engineering techniques. It combines compelling perspectives, guidelines, and frameworks, applying statistical and mathematical models. As industries and research communities witness increasing demand for web-based systems, software modules, heuristic models, and survey analysis, the book emphasizes the critical methodologies associated with data verification, reliability, fault tolerance, and viability.

The current data engineering demands more than theoretical understanding; it necessitates a practical, nuanced approach. Data engineering involves the intricate orchestration of systems and architectural frameworks for collecting, storing, processing, and analyzing vast datasets. The challenge lies in ensuring this data is managed and harnessed effectively, fostering insightful knowledge and steering organizations toward data-driven decision-making. Critical Approaches to Data Engineering Systems and Analysis unveils the latent potential inherent in diverse data analysis and engineering techniques. It combines compelling perspectives, guidelines, and frameworks, applying statistical and mathematical models. As industries and research communities witness increasing demand for web-based systems, software modules, heuristic models, and survey analysis, the book emphasizes the critical methodologies associated with data verification, reliability, fault tolerance, and viability.

In the environment of energy systems, the effective utilization of both conventional and renewable sources poses a major challenge. The integration of microgrid systems, crucial for harnessing energy from distributed sources, demands intricate solutions due to the inherent intermittency of these sources. Academic scholars engaged in power system research find themselves at the forefront of addressing issues such as energy source estimation, coordination in dynamic environments, and the effective utilization of artificial intelligence (AI) techniques. Intelligent Solutions for Sustainable Power Grids focuses on emerging research areas, this book addresses the uncertainty of renewable energy sources, employs state-of-the-art forecasting techniques, and explores the application of AI techniques for enhanced power system operations. From economic aspects to the digitalization of power systems, the book provides a holistic approach. Tailored for undergraduate and postgraduate students as well as seasoned researchers, it offers a roadmap to navigate the intricate landscape of modern power systems. Dive into a wealth of knowledge encompassing smart energy systems, renewable energy integration, stability analysis of microgrids, power quality enhancement, and much more. This book is not just a guide; it is the solution to the pressing challenges in the dynamic field of energy systems.

Information retrieval (IR) is considered to be the science of searching for information from a variety of information sources related to texts, images, sounds, or multimedia. With the rise of the internet and digital databases, updated information retrieval methodologies are essential to ensure the continued facilitation and enhancement of information exchange. Critical Approaches to Information Retrieval Research is a critical scholarly publication that provides multidisciplinary examinations of theoretical innovations and methods in information retrieval technologies including search and storage applications for data, text, image, sound, document, and video retrieval. Featuring a wide range of topics including data mining, machine learning, and ontology, this book is ideal for librarians, software engineers, data scientists, professionals, researchers, information engineers, scientists, practitioners, and academicians working in the fields of computer science, information technology, information and communication sciences, education, health, library, and more.

* Provides a coherent set of reference points for students to see the issues at levels of theory, method and practice * Presents practical examples of critical research and demonstrates the lessons learnt from applying a critical approach. * Cutting edge book with newly commissioned international team of authors

This comprehensive handbook offers a broad overview of contemporary research on engineering education and its practical application. Over the past two decades, the field of engineering education research (EER) has become a vibrant and impactful community with new journals, conferences, and doctoral and research programs established across the globe. The increased interest in this area has helped improve the education and training of the next generation of engineers, as well as supporting growth in the use of technology for teaching and learning, increased attention to broadening participation, diversity and inclusion in the field, and a wide international expansion of the field. Drawing on the work of 100 expert contributors from over 20 countries, this volume covers both emergent and established areas of research within engineering education, giving voice to newcomers to the field as well as perspectives from established experts. Contents include: Sociocognitive and affective perspectives on engineering education. Technology and online learning in engineering education. Cultural and ethical issues including diversity, equity, and inclusion in engineering education. Curriculum design, teaching practices, and teacher education at all levels. Research methods and assessment in engineering education. This book offers an innovative and in-depth overview of engineering education scholarship and practice, which will be of use to researchers in engineering education, engineering educators and faculty, teacher educators in engineering education or STEM education, and other engineering and STEM-related professional organizations. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

Since the emerging discipline of engineering enterprise systems extends traditional systems engineering to develop webs of systems and systems-of-systems, the engineering management and management science communities need new approaches for analyzing and managing risk in engineering enterprise systems. Advanced Risk Analysis in Engineering Enterprise Systems presents innovative methods to address these needs. With a focus on engineering management, the book explains how to represent, model, and measure risk in large-scale, complex systems that are engineered to function in enterprise-wide environments. Along with an analytical framework and computational model, the authors introduce new protocols: the risk co-relationship (RCR) index and the functional dependency network analysis (FDNA) approach. These protocols capture dependency risks and risk co-relationships that may exist in an enterprise. Moving on to extreme and rare event risks, the text discusses how uncertainties in system behavior are intensified in highly networked, globally connected environments. It also describes how the risk of extreme latencies in delivering time-critical data, applications, or services can have catastrophic consequences and explains how to avoid these events. With more and more communication, transportation, and financial systems connected across domains and interfaced with an infinite number of users, information repositories, applications, and services, there has never been a greater need for analyzing risk in engineering enterprise systems. This book gives you advanced methods for tackling risk problems at the enterprise level.

This book constitutes the refereed proceedings of the 10th International Conference on Model and Data Engineering, MEDI 2021, held in Tallinn, Estonia, in June 2021. The 16 full papers and 8 short papers presented in this book were carefully reviewed and selected from 47 submissions. Additionally, the volume includes 3 abstracts of invited talks. The papers cover broad research areas on both theoretical, systems and practical aspects. Some papers include mining complex databases, concurrent systems, machine learning, swarm optimization, query processing, semantic web, graph databases, formal methods, model-driven engineering, blockchain, cyber physical systems, IoT applications, and smart systems. Due to the Corona pandemic the conference was held virtually.