| data engineering research papers: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| data engineering research papers: Recent Advances in Artificial Intelligence and Data Engineering Pushparaj Shetty D., Surendra Shetty, 2021-10-31 This book presents select proceedings of the International Conference on Artificial Intelligence and Data Engineering (AIDE 2020). Various topics covered in this book include deep learning, neural networks, machine learning, computational intelligence, cognitive computing, fuzzy logic, expert systems, brain-machine interfaces, ant colony optimization, natural language processing, bioinformatics and computational biology, cloud computing, machine vision and robotics, ambient intelligence, intelligent transportation, sensing and sensor networks, big data challenge, data science, high performance computing, data mining and knowledge discovery, and data privacy and security. The book will be a valuable reference for beginners, researchers, and professionals interested in artificial intelligence, robotics and data engineering. |
| data engineering research papers: Data Science: From Research to Application Mahdi Bohlouli, Bahram Sadeghi Bigham, Zahra Narimani, Mahdi Vasighi, Ebrahim Ansari, 2020-01-28 This book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0. Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017. This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to share ideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems. Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians. |
| data engineering research papers: Advances in Artificial Intelligence and Data Engineering Niranjan N. Chiplunkar, Takanori Fukao, 2021-08-16 This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering. |
| data engineering research papers: Proceedings of the International Conference on Data Engineering and Communication Technology Suresh Chandra Satapathy, Vikrant Bhateja, Amit Joshi, 2016-08-24 This two-volume book contains research work presented at the First International Conference on Data Engineering and Communication Technology (ICDECT) held during March 10–11, 2016 at Lavasa, Pune, Maharashtra, India. The book discusses recent research technologies and applications in the field of Computer Science, Electrical and Electronics Engineering. The aim of the Proceedings is to provide cutting-edge developments taking place in the field data engineering and communication technologies which will assist the researchers and practitioners from both academia as well as industry to advance their field of study. |
| data engineering research papers: Large Scale and Big Data Sherif Sakr, Mohamed Gaber, 2014-06-25 Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments. The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security. Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing. After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques. |
| data engineering research papers: Intelligent Data Engineering and Automated Learning – IDEAL 2020 Cesar Analide, Paulo Novais, David Camacho, Hujun Yin, 2020-10-29 This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic. |
| data engineering research papers: Data, Engineering and Applications Sanjeev Sharma, Sheng-Lung Peng, Jitendra Agrawal, Rajesh K. Shukla, Dac-Nhuong Le, 2022-10-11 The book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications. |
| data engineering research papers: Data Engineering Yupo Chan, John Talburt, Terry M. Talley, 2009-10-15 DATA ENGINEERING: Mining, Information, and Intelligence describes applied research aimed at the task of collecting data and distilling useful information from that data. Most of the work presented emanates from research completed through collaborations between Acxiom Corporation and its academic research partners under the aegis of the Acxiom Laboratory for Applied Research (ALAR). Chapters are roughly ordered to follow the logical sequence of the transformation of data from raw input data streams to refined information. Four discrete sections cover Data Integration and Information Quality; Grid Computing; Data Mining; and Visualization. Additionally, there are exercises at the end of each chapter. The primary audience for this book is the broad base of anyone interested in data engineering, whether from academia, market research firms, or business-intelligence companies. The volume is ideally suited for researchers, practitioners, and postgraduate students alike. With its focus on problems arising from industry rather than a basic research perspective, combined with its intelligent organization, extensive references, and subject and author indices, it can serve the academic, research, and industrial audiences. |
| data engineering research papers: DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED Siddharth Konkimalla, MANIKANTH SARISA, MOHIT SURENDER REDDY, SANJAY BAUSKAR, .The advances in data engineering technologies, including big data infrastructure, knowledge graphs, and mechanism design, will have a long-lasting impact on artificial intelligence (AI) research and development. This paper introduces data engineering in AI with a focus on the basic concepts, applications, and emerging frontiers. As a new research field, most data engineering in AI is yet to be properly defined, and there are abundant problems and applications to be explored. The primary purpose of this paper is to expose the AI community to this shining star of data science, stimulate AI researchers to think differently and form a roadmap of data engineering for AI. Since this is primarily an informal essay rather than an academic paper, its coverage is limited. The vast majority of the stimulating studies and ongoing projects are not mentioned in the paper. |
| data engineering research papers: Computational Methods and Data Engineering Vijendra Singh, Vijayan K. Asari, Sanjay Kumar, R. B. Patel, 2020-11-04 This book gathers selected high-quality research papers from the International Conference on Computational Methods and Data Engineering (ICMDE 2020), held at SRM University, Sonipat, Delhi-NCR, India. Focusing on cutting-edge technologies and the most dynamic areas of computational intelligence and data engineering, the respective contributions address topics including collective intelligence, intelligent transportation systems, fuzzy systems, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing. |
| data engineering research papers: Intelligent Data Engineering and Analytics Vikrant Bhateja, Fiona Carroll, João Manuel R. S. Tavares, Sandeep Singh Sengar, Peter Peer, 2023-11-25 The book presents the proceedings of the 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2023), held at Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales, UK, during April 11–12, 2023. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols, and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines. |
| data engineering research papers: Big Data Analytics and Knowledge Discovery Matteo Golfarelli, Robert Wrembel, Gabriele Kotsis, A Min Tjoa, Ismail Khalil, 2021-09-04 This volume LNCS 12925 constitutes the papers of the 23rd International Conference on Big Data Analytics and Knowledge Discovery, held in September 2021. Due to COVID-19 pandemic it was held virtually. The 12 full papers presented together with 15 short papers in this volume were carefully reviewed and selected from a total of 71 submissions. The papers reflect a wide range of topics in the field of data integration, data warehousing, data analytics, and recently big data analytics, in a broad sense. The main objectives of this event are to explore, disseminate, and exchange knowledge in these fields. |
| data engineering research papers: Data Engineering with Google Cloud Platform Adi Wijaya, 2022-03-31 Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book. |
| data engineering research papers: Intelligent Data Analysis Michael R. Berthold, David J Hand, 2007-06-07 This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes. |
| data engineering research papers: Data Engineering and Applications Jitendra Agrawal, |
| data engineering research papers: Data-Driven Engineering Design Ang Liu, Yuchen Wang, Xingzhi Wang, 2021-10-09 This book addresses the emerging paradigm of data-driven engineering design. In the big-data era, data is becoming a strategic asset for global manufacturers. This book shows how the power of data can be leveraged to drive the engineering design process, in particular, the early-stage design. Based on novel combinations of standing design methodology and the emerging data science, the book presents a collection of theoretically sound and practically viable design frameworks, which are intended to address a variety of critical design activities including conceptual design, complexity management, smart customization, smart product design, product service integration, and so forth. In addition, it includes a number of detailed case studies to showcase the application of data-driven engineering design. The book concludes with a set of promising research questions that warrant further investigation. Given its scope, the book will appeal to a broad readership, including postgraduate students, researchers, lecturers, and practitioners in the field of engineering design. |
| data engineering research papers: Intelligent Data Engineering and Automated Learning - IDEAL 2009 Emilio Corchado, Hujun Yin, 2009-09-07 This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications. |
| data engineering research papers: Machine Learning and Data Science Prateek Agrawal, Charu Gupta, Anand Sharma, Vishu Madaan, Nisheeth Joshi, 2022-07-25 MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation. |
| data engineering research papers: Model and Data Engineering Ladjel Bellatreche, Filipe Mota Pinto, 2011-09-15 This book constitutes the refereed proceedings of the First International Conference on Model and Data Engineering, MEDI 2011, held in Óbidos, Portugal, in September 2011. The 18 revised full papers presented together with 8 short papers and three keynotes were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on ontology engineering; Web services and security; advanced systems; knowledge management; model specification and verification; and models engineering. |
| data engineering research papers: Enterprise Big Data Engineering, Analytics, and Management Atzmueller, Martin, 2016-06-01 The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field. |
| data engineering research papers: Supervised and Unsupervised Data Engineering for Multimedia Data Suman Kumar Swarnkar, J. P. Patra, Sapna Singh Kshatri, Yogesh Kumar Rathore, Tien Anh Tran, 2024-05-07 SUPERVISED and UNSUPERVISED DATA ENGINEERING for MULTIMEDIA DATA Explore the cutting-edge realms of data engineering in multimedia with Supervised and Unsupervised Data Engineering for Multimedia Data, where expert contributors delve into innovative methodologies, offering invaluable insights to empower both novices and seasoned professionals in mastering the art of manipulating multimedia data with precision and efficiency. Supervised and Unsupervised Data Engineering for Multimedia Data presents a groundbreaking exploration into the intricacies of handling multimedia data through the lenses of both supervised and unsupervised data engineering. Authored by a team of accomplished experts in the field, this comprehensive volume serves as a go-to resource for data scientists, computer scientists, and researchers seeking a profound understanding of cutting-edge methodologies. The book seamlessly integrates theoretical foundations with practical applications, offering a cohesive framework for navigating the complexities of multimedia data. Readers will delve into a spectrum of topics, including artificial intelligence, machine learning, and data analysis, all tailored to the challenges and opportunities presented by multimedia datasets. From foundational principles to advanced techniques, each chapter provides valuable insights, making this book an essential guide for academia and industry professionals alike. Whether you’re a seasoned practitioner or a newcomer to the field, Supervised and Unsupervised Data Engineering for Multimedia Data illuminates the path toward mastery in manipulating and extracting meaningful insights from multimedia data in the modern age. |
| data engineering research papers: Intelligent Data Engineering and Automated Learning – IDEAL 2019 Hujun Yin, David Camacho, Peter Tino, Antonio J. Tallón-Ballesteros, Ronaldo Menezes, Richard Allmendinger, 2019-11-07 This two-volume set of LNCS 11871 and 11872 constitutes the thoroughly refereed conference proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019, held in Manchester, UK, in November 2019. The 94 full papers presented were carefully reviewed and selected from 149 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2019 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspired models (including neural networks, evolutionary computation and swarm intelligence), agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. |
| data engineering research papers: Model and Data Engineering Philippe Fournier-Viger, Ahmed Hassan, Ladjel Bellatreche, 2022-11-18 This book constitutes the refereed proceedings of the 11th International Conference on Model and Data Engineering, MEDI 2022, held in Cairo, Egypt, in November 2022. The 18 full papers presented in this book were carefully reviewed and selected from 65 submissions. The papers cover topics such as database systems, data stream analysis, knowledge-graphs, machine learning, model-driven engineering, image processing, diagnosis, natural language processing, optimization, and advanced applications such as the internet of things and healthcare. |
| data engineering research papers: Data Engineering and Communication Technology K. Srujan Raju, Roman Senkerik, Satya Prasad Lanka, V. Rajagopal, 2020-01-08 This book includes selected papers presented at the 3rd International Conference on Data Engineering and Communication Technology (ICDECT-2K19), held at Stanley College of Engineering and Technology for Women, Hyderabad, from 15 to 16 March 2019. It features advanced, multidisciplinary research towards the design of smart computing, information systems, and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence, and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment, and industry. |
| data engineering research papers: Software Engineering in IoT, Big Data, Cloud and Mobile Computing Haengkon Kim, Roger Lee, 2020-12-26 This edited book presents scientific results of the International Semi-Virtual Workshop on Software Engineering in IoT, Big data, Cloud and Mobile Computing (SE-ICBM 2020) which was held on October 15, 2020, at Soongsil University, Seoul, Korea. The aim of this workshop was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The workshop organizers selected the best papers from those papers accepted for presentation at the workshop. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 17 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science. |
| data engineering research papers: Emerging Research in Data Engineering Systems and Computer Communications P. Venkata Krishna, Mohammad S. Obaidat, 2020-02-10 This book gathers selected papers presented at the 2nd International Conference on Computing, Communications and Data Engineering, held at Sri Padmavati Mahila Visvavidyalayam, Tirupati, India from 1 to 2 Feb 2019. Chiefly discussing major issues and challenges in data engineering systems and computer communications, the topics covered include wireless systems and IoT, machine learning, optimization, control, statistics, and social computing. |
| data engineering research papers: Intelligent Data Engineering and Analytics Suresh Chandra Satapathy, Yu-Dong Zhang, Vikrant Bhateja, Ritanjali Majhi, 2020-08-29 This book gathers the proceedings of the 8th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2020), held at NIT Surathkal, Karnataka, India, on 4–5 January 2020. In these proceedings, researchers, scientists, engineers and practitioners share new ideas and lessons learned in the field of intelligent computing theories with prospective applications in various engineering disciplines. The respective papers cover broad areas of the information and decision sciences, and explore both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. Given its scope, the book offers a valuable resource for graduate students in various engineering disciplines. |
| data engineering research papers: The Engineering Index John Butler Johnson, Henry Harrison Suplee, Johannes H. Cuntz, Charles Buxton Going, 1901 |
| data engineering research papers: Model and Data Engineering Mohamed Mosbah, Tahar Kechadi, Ladjel Bellatreche, Faiez Gargouri, 2024-01-22 This volume LNCS 14396 constitutes the refereed proceedings of the 12th International Conference, MEDI 2023,in November 2023 ,held in Sousse, Tunisia. The 27 full papers were carefully peer reviewed and selected from 99 submissions. The Annual International Conference on Model and Data Engineering focuses on bring together researchers and practitioners and enabling them to showcase the latest advances in modelling and data management. |
| data engineering research papers: Model and Data Engineering Christian Attiogbé, Sadok Ben Yahia, 2021-06-14 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. |
| data engineering research papers: Research Data Access and Management in Modern Libraries Bhardwaj, Raj Kumar, Banks, Paul, 2019-05-15 Handling and archiving data should be done in a highly professional and quality-controlled manner. For academic and research libraries, it is required to know how to document data and support traceability, as well as to make it reusable and productive. However, these institutions have different requirements relating to the archiving and reusability of data. Therefore, a comprehensive source of information is required to understand data access and management within these organizations. Research Data Access and Management in Modern Libraries is a critical scholarly resource that delves into innovative data management strategies and strategy implementation in library settings and provides best practices to stakeholders using the latest tools and technology. It further explores concepts such as research data management, data access, data preservation, building document and data institutional repositories, applications of Web 2.0 tools, mobile technology applications in data access, and conducting information literacy programs. This book is ideal for librarians, information specialists, research scholars, students, IT managers, computer scientists, policymakers, educators, and academic administrators. |
| data engineering research papers: Cybersecurity and Evolutionary Data Engineering Raj Jain, Carlos M. Travieso, Sanjeev Kumar, 2023-09-19 This book comprises the select proceedings of the 2nd International Conference on Cybersecurity and Evolutionary Data Engineering (ICCEDE 2022). The contents highlight cybersecurity and digital forensics, evolutionary data engineering, and data management for secure contemporary applications. It includes papers on data models, semantics, query language; AI-driven industrial automation, ERP, CRM data security; authentication and access control; cyberspace structure and models; and drone large data filtration, cleansing, and security, among others. This book is of immense interest to researchers in academia and industry working in the fields of electronics and data engineering. |
| data engineering research papers: Model and Data Engineering Yamine Ait Ameur, Ladjel Bellatreche, George A. Papadopoulos, 2014-09-19 This book constitutes the refereed proceedings of the 4th International Conference on Model and Data Engineering, MEDI 2014, held in Larnaca, Cyprus, in September 2014. The 16 long papers and 12 short papers presented together with 2 invited talks were carefully reviewed and selected from 64 submissions. The papers specifically focus on model engineering and data engineering with special emphasis on most recent and relevant topics in the areas of modeling and models engineering; data engineering; modeling for data management; and applications and tooling. |
| data engineering research papers: Knowledge-Intensive Economies and Opportunities for Social, Organizational, and Technological Growth Lytras, Miltiadis D., Daniela, Linda, Visvizi, Anna, 2018-10-12 The modern world is developing at a pace where few can thoroughly keep track of its progress. More advancements in technology, evolving standards of education, and ongoing cultural and societal developments are leading to a need for improved pathways of knowledge discovery and dissemination. Knowledge-Intensive Economies and Opportunities for Social, Organizational, and Technological Growth provides emerging research exploring how academic research can represent both a bold response to the problems society faces today and a source of alternative solutions to those problems. This publication is derived from the basic understanding that education plays the role of the key enabler in the process of navigating these contemporary challenges. Featuring coverage on a broad range of topics such as e-service exploration, progressive online learning in urban areas, and advances in multimedia sharing, this book is ideally designed for consultants, academics, industry professionals, policymakers, politicians, and government officials seeking current research on the impact of information technology and the knowledge-based era. |
| data engineering research papers: AI-DRIVEN DATA ENGINEERING TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHT Eswar Prasad Galla, Chandrababu Kuraku, Hemanth Kumar Gollangi, Janardhana Rao Sunkara, Chandrakanth Rao Madhavaram, ..... |
| data engineering research papers: Model and Data Engineering Alfredo Cuzzocrea, Sofian Maabout, 2013-09-10 This book constitutes the refereed proceedings of the Third International Conference on Model and Data Engineering, MEDI 2013, held in Amantea, Calabria, Italy, in September 2013. The 19 long papers and 3 short papers presented were carefully reviewed and selected from 61 submissions. The papers specifically focus on model engineering and data engineering with special emphasis on most recent and relevant topics in the areas of model-driven engineering, ontology engineering, formal modeling, security, and database modeling. |
| data engineering research papers: Advances in Artificial Intelligence and Data Engineering Niranjan N. Chiplunkar, Takanori Fukao, 2020-08-13 This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering. |
| data engineering research papers: Data Engineering and Communication Technology K. Ashoka Reddy, B. Rama Devi, Boby George, K. Srujan Raju, 2021-05-23 This book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25–26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry. |
| data engineering research papers: Data Management Technologies and Applications Alfredo Cuzzocrea, Oleg Gusikhin, Slimane Hammoudi, Christoph Quix, 2023-08-23 This book constitutes the refereed post-proceedings of the 10th International Conference and 11th International Conference on Data Management Technologies and Applications, DATA 2021 and DATA 2022, was held virtually due to the COVID-19 crisis on July 6–8, 2021 and in Lisbon, Portugal on July 11-13, 2022. The 11 full papers included in this book were carefully reviewed and selected from 148 submissions. They were organized in topical sections as follows: engineers and practitioners interested on databases, big data, data mining, data management, data security and other aspects of information systems and technology involving advanced applications of data. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Methodology Section for Research Papers - San José State …
Methodology Section for Research Papers The methodology section of your paper describeshow your research was conducted. This information allows readers to check ... research question …
Research Methods for Engineers - Cambridge University Press …
ducting high-quality engineering research. ... is the author of over 120 papers published in interna-tional journals and is a Fellow of the Institution of Engineers ... back, but had the additional …
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yet making effects the product business. [1]The new patterns in software engineering exploration themes settle under the examination field of Cloud Computing, Big Data, Android Computing, …
How to write for - IEEE
But that can only happen if your research can be read, understood, and built upon by your fellow researchers and engineers. This guide is designed to help you succeed as an author. ... if you …
Research methods in engineering design: a synthesis of
The analysis of scientic papers about research in engineering design performed presented in this paper aims to contribute to this aim. There are many possible ways to analyse, categorise or ...
Book - Engineering Research Methodology
3 3.2. Fusion of Science, Research And Technology 3.2.1. Aristotle's Distinctions between Science and Technology 3.2.2. Objects: Unchangeable vs. Changeable
6G Software Engineering: A Systematic Mapping Study
Finally, 18 papers were included. 2.2 Data Extraction To summarize the included papers, we extracted relevant data infor-mation when doing the full read part. In order to better distinguish …
LLMs for Data Engineering on Enterprise Data - GitHub Pages
LLMs for Data Engineering on Enterprise Data ... research primarily uses datasets based on tables from web sources such as Wikipedia, calling the applicability of LLMs for real-world ...
Data Visualization: A Study of Tools and Challenges - AJTMR
It can be used in producing research related data, health data, energy consumptions, educational budgetary analysis, fraud detection representation and many more ... (2016) "Data …
A Review of Reverse Engineering Theories and Tools - ijesi.org
understanding about how this information is processed, data reverse engineering tackles the question of what information is stored and how this information can be used in a different …
Research Methodology and IPR (GR20D5011) - Gokaraju …
Department of Civil Engineering RESEARCH METHODOLOGY AND IPR ( GR 20D5011) COURSE FILE CHECK LIS S.No. Name of the Format Page ... Mid-I and Mid-II question …
What makes a great engineering paper? Editorial insights …
high-quality engineering research papers. I t is not easy to make a research manuscript a compellingread.Theyaretechnical,theydon’t ... data explored, and with mechanisms underlying
Introduction to Data Analysis Handbook - ed
Data Analysis Handbook Migrant & Seasonal Head Start Technical Assistance Center Academy for Educational Development “If I knew what ... under represented, at-risk children and families …
Reverse Engineering: A Brief Run-Down
But today, reverse engineering is applied for lots of legitimate applications [1].The reverse engineering process starts with an executable program. Fig 1: Reverse engineering and Re …
Predictive Models in Software Engineering: Challenges and …
informed decisions to solve a problem or conduct research using predictive models. This paper contributes to the research on predictive models by performing a comprehensive systematic …
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engineering research and development has also targeted for driver’s safety. The governing body of Formula 1, i.e. Fédération Internationale de l'Automobile (FIA) has made significant rule …
Abstract arXiv:1706.00933v7 [cs.SE] 10 Jul 2019
Software engineering research is evolving and papers are increasingly based on empirical data from a multi-tude of sources, using statistical tests to determine if and to what degree empirical …
2024 ASEE Biomedical Engineering Division – Call for Papers
biomedical engineering education and research We accept three types of papers: work in progress (WIP) papers, evidence-based practice papers, research papers. New this year: a …
Research Methodology(R22DHS53) - MRCET
Page 4 of 6 5 Examine the importance, characteristics of research design and how research designs are classified and brief on how experimental design is different from a descriptive …
Stock Market Analysis Using Data Science: A Survey - IJFMR
1Assistant Professor, School of Engineering Ajeenkya D.Y.Patil.University Pune,India 2,3,4School of Engineering Ajeenkya D.Y.Patil.University Pune,India Abstract This research paper employs …
CHBE 410 Statistics & Design of Experiments - UMD
1. Identify and apply appropriate statistical tools or models to the analysis of engineering and research data. 2. Read and critically analyze results presented in engineering research papers …
Empirical Research In Software Engineering: Concepts, …
earned her master’s and doctorate degrees in software engineering from the University School of Information Technology of Guru Gobind Singh Indraprastha University. She received the IBM …
Data Cleaning: Problems and Current Approaches - Better …
data cleaning and other data transformations should be specified in a declarative way and be reusable for other data sources as well as for query processing. Especially for data …
The twenty-first century of structural engineering research: A …
structural engineering research. 3. Article-abstract data in structural engineering research Structural engineering is defined as a discipline that deals with the analysis and design of …
This capstone paper is posted as an example of the type of …
Assistant Professor Bethany Brooke Cutts, Research Director Piper Hodson, Director of the NRES Online M.S. Program Associate Professor Lulu Rodriguez This capstone paper is …
A Critical Review of Data Warehouse - ripublication.com
Keywords: Data Warehouse, Online Analytical Processing (OLAP). Introduction Data warehouse is a Data repository containing historical data from heterogeneous sources. It is designed for …
Comparison of Stack and Queue Data structures - IRJET
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 ... These data may be of …
Results Section for Research Papers - San José State University
Results Section for Research Papers, Summer 2022. 1 of 6 Results Section for Research Papers The results (or findings) section is one of the most important parts of a research paper, in …
How to Read a Computer Science Research Paper
engineering). Research papers that date from before the 1990s may appear very different from newer papers. They may be more speculative, include less evaluation or no evaluation, and …
Charging the Future: Challenges and Opportunities for Electric …
The views expressed in the HKS Faculty Research Working Paper Seriesare those of the author(s) and do not necessarily reflect those of the John F. Kennedy School of Government or …
Deep Generative Models in Engineering Design: A Review
public datasets in engineering is severely lacking. Fur-thermore, even when data is available, the distribution of the data often does not cover the design space evenly with much sparsity often …
AI4VIS: Survey on Artificial Intelligence Approaches for Data …
several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of …
Evolution of statistical analysis in empirical software …
Software engineering research is evolving and papers are increasingly based on empirical data from a multi-tude of sources, using statistical tests to determine if and to what degree empirical …
What Makes Good Research in Software Engineering? - CMU …
research are often quality, cost, and timeliness of software products. This section presents a model that explains software engineering research papers by classifying the types of research …
The General Index of Software Engineering Papers - arXiv.org
The dataset serves use cases in the field of meta-research, allow-ing to introspect the output of software engineering research even when access to papers or scholarly search engines is not …
Software Engineering for Machine Learning: A Case Study
and research [1], [9], [10], [11]. It has commonalities with prior workflows defined in the context of data science and data mining, such as TDSP [12], KDD [13], and CRISP-DM [14]. Despite …
Digital Twin: Enabling Technologies, Challenges and Open …
a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these …
Qualitative Research Methods in Engineering - American …
research methods, from data acquisition, data analysis, sample size, validity and so on. The research found within engineering programs tends to grounded firmly in the positivist paradigm …
How to Read a Computer Science Research Paper
engineering). Research papers that date from before the 1990s may appear very different from newer papers. They may be more speculative, include less evaluation or no evaluation, and …
Data and Information Quality Research: Its Evolution and Future
Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research. ... As a …
Data Structures And Its Limitations - IRE Journals
Data structure is a method of structuring of data for easy usage and retrieval. The basic aim of data ... IRE 1701797 ICONIC RESEARCH AND ENGINEERING JOURNALS 44 Advantages of …
2023 IEEE 16th International Conference on Cloud Computing …
CLOUD Conference Papers Cloud & AI - I (CLD_CON1) xCloudServing: Automated ML Serving Across Clouds 1 Malgorzata Lazuka (IBM Research - Europe; ETH Zurich, Switzerland), …
Study of Trends of Industrial Engineering Research - IOSR-JEN
this paper, the results of a research to study the position and trend of Industrial Engineering research in recent years are described. The data from a sample of 7,000 IE-related articles …
Environmental Engineering with Data Science - International …
Once data is collected, it must be analyzed using appropriate data analysis techniques to provide meaningful insights and conclusions. Several data analysis methods can be used in …
IPL Score Prediction & Analysis - IJFMR
approach to this challenge. This research paper aims to explore the intricacies of developing an effective IPL score prediction model, which leverages historical data, match conditions, player …
Writing Good Software Engineering Research Papers
Software engineering researchers solve problems of several different kinds. To do so, they produce several different kinds of results, and they should develop appropriate evidence to …
Lakehouse: A New Generation of Open Platforms that Unify …
Data Systems Research (CIDR ’21), January 11–15, 2021, Online. quality and governance downstream. In this architecture, a small subset of data in the lake would later be ETLed to a …
Engineering Blockchain Based Software Systems: …
This article presents a systematic literature review of the state-of-the-art in BBS engineering research from the perspective of the software engineering discipline. We characterize BBS …
How Practitioners Perceive the Relevance of Software …
This includes 184 papers from the meetings of ESEC/FSE and FSE (2009-2013), and 387 papers from ICSE (2010-2014), for a total of 571 papers. We believe that this is a representative …
Artificial Intelligence In Civil Engineering - ijcrt.org
The origins of AI research in 1956 and its development as a multidisciplinary field. The four stages of AI research, from the 1950s to the 1990s the main theories and methods of AI, including …
