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Data Management, Analytics and Innovation: Proceedings of ICDMAI 2023 (Lecture Notes in Networks and Systems #662)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. The volume is a collection of peer reviewed research papers presented at Seventh International Conference on Data Management, Analytics and Innovation (ICDMAI 2023), held during 20 – 22 January, 2023 in Pune, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 1 (Lecture Notes in Networks and Systems #997)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 2 (Lecture Notes in Networks and Systems #998)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Migration Management for SAP S/4HANA: A Practical Guide
by Aleksei ArziaevEnhance your data transfer and storage skills with this comprehensive step-by-step guide to managing data migration for new on-premises SAP S/4HANA implementations. This book is tailored towards small to large projects, with a focus on the managerial aspects of the data migration process rather than the technical details. You’ll follow a project-led approach, enriched with a practical case study, and a comprehensive methodology for data migration planning and documentation. Athen traverse through a detailed plan on managing and documenting data migration throughout the project lifecycle. This book utilizes the general SAP Activate methodology for on-premises solutions as its foundational framework, enhancing it with specific strategies for data migration. Structured in alignment with the project phases of the SAP Activate methodology, Data Migration Management for SAP S/4HANA methodically covers planning, organizing, and controlling the data migration process. It serves as an essential guide for professionals tasked with implementing SAP S/4HANA in their business, ensuring a thorough understanding of each data migration phase on the project. What You'll Learn Significantly decrease the time needed for both the preparation and execution of data migration activities. Foster clear transparency in data migration processes for all stakeholders, including the customer and the project team. Facilitate a seamless and timely data migration process. Establish a benchmark for data migration management in future projects. Address and remedy any deficiencies in the SAP Activate methodology pertaining to data migration. Who This Book Is For SAP projects and data migration workstreams leads, already well-versed in SAP Activate methodology and possessing moderate experience in project and workstream management, who are seeking to enhance their skills in professionally managing data migration in implementation projects.
Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17–22, 2018, Proceedings (Lecture Notes in Computer Science #10943)
by Ying Tan Yuhui Shi Qirong TangThis book constitutes the refereed proceedings of the Third International Conference on Data Mining and Big Data, DMBD 2018, held in Shanghai, China, in June 2018. The 74 papers presented in this volume were carefully reviewed and selected from 126 submissions. They are organized in topical sections named: database, data preprocessing, matrix factorization, data analysis, visualization, visibility analysis, clustering, prediction, classification, pattern discovery, text mining and knowledge management, recommendation system in social media, deep learning, big data, Industry 4.0, practical applications
Data Mining and Machine Learning in High-Performance Sport: Performance Analysis of On-field and Video Assistant Referees in European Soccer Leagues (SpringerBriefs in Applied Sciences and Technology)
by Mohamad Razali Abdullah Mohd Azraai Mohd Razman Rabiu Muazu Musa Anwar P.P. Abdul Majeed Garry KuanThis book explores the application of data mining and machine learning techniques in studying the activity pattern, decision-making skills, misconducts, and actions resulting in the intervention of VAR in European soccer leagues referees. The game of soccer at the elite level is characterised by intense competitions, a high level of intensity, technical, and tactical skills coupled with a long duration of play. Referees are required to officiate the game and deliver correct and indisputable decisions throughout the duration of play. The increase in the spatial and temporal task demands of the game necessitates that the referees must respond and cope with the physiological and psychological loads inherent in the game. The referees are also required to deliver an accurate decision and uphold the rules and regulations of the game during a match. These demands and attributes make the work of referees highly complex. The increasing pace and complexity of the game resulted in the introduction of the Video Assistant Referee (VAR) to assist and improve the decision-making of on-field referees. Despite the integration of VAR into the current refereeing system, the performances of the referees are yet to be error-free. Machine learning coupled with data mining techniques has shown to be vital in providing insights from a large dataset which could be used to draw important inferences that can aid decision-making for diagnostics purposes and overall performance improvement. A total of 6232 matches from 5 consecutive seasons officiated across the English Premier League, Spanish LaLiga, Italian Serie A as well as the German Bundesliga was studied. It is envisioned that the findings in this book could be useful in recognising the activity pattern of top-class referees, that is non-trivial for the stakeholders in devising strategies to further enhance the performances of referees as well as empower talent identification experts with pertinent information for mapping out future high-performance referees.
Data Mining and Machine Learning in Sports: Success Metrics for Elite Goalkeepers in European Football Leagues (SpringerBriefs in Applied Sciences and Technology)
by Rabiu Muazu Musa Mohamad Razali Abdullah Anwar P. Majeed Aina Munirah Ab RasidThis brief highlights the factors associated with good goalkeeping techniques and their impact on goalkeepers’ performance in elite European football leagues. The goalkeeping performances of 1600 goalkeepers from five consecutive seasons across the English Premier League, Spanish La Liga, Italian Serie A, and German Bundesliga are studied. The findings from this brief are useful for identifying the success metrices of top-class goalkeepers that help stakeholders to devise strategies to further enhance their performances and empower talent identification experts with pertinent information for mapping out future high-performance goalkeepers.
Data Mining for Co-location Patterns: Principles and Applications
by Guoqing ZhouCo-location pattern mining detects sets of features frequently located in close proximity to each other. This book focuses on data mining for co-location pattern, a valid method for identifying patterns from all types of data and applying them in business intelligence and analytics. It explains the fundamentals of co-location pattern mining, co-location decision tree, and maximal instance co-location pattern mining along with an in-depth overview of data mining, machine learning, and statistics. This arrangement of chapters helps readers understand the methods of co-location pattern mining step-by-step and their applications in pavement management, image classification, geospatial buffer analysis, etc.
Data Mining for Service (Studies in Big Data #3)
by Katsutoshi YadaVirtually all nontrivial and modern service related problems and systems involve data volumes and types that clearly fall into what is presently meant as "big data", that is, are huge, heterogeneous, complex, distributed, etc. Data mining is a series of processes which include collecting and accumulating data, modeling phenomena, and discovering new information, and it is one of the most important steps to scientific analysis of the processes of services. Data mining application in services requires a thorough understanding of the characteristics of each service and knowledge of the compatibility of data mining technology within each particular service, rather than knowledge only in calculation speed and prediction accuracy. Varied examples of services provided in this book will help readers understand the relation between services and data mining technology. This book is intended to stimulate interest among researchers and practitioners in the relation between data mining technology and its application to other fields.
Data Mining for Social Robotics: Toward Autonomously Social Robots (Advanced Information and Knowledge Processing #0)
by Yasser Mohammad Toyoaki NishidaThis book explores an approach to social robotics based solely on autonomous unsupervised techniques and positions it within a structured exposition of related research in psychology, neuroscience, HRI, and data mining. The authors present an autonomous and developmental approach that allows the robot to learn interactive behavior by imitating humans using algorithms from time-series analysis and machine learning. The first part provides a comprehensive and structured introduction to time-series analysis, change point discovery, motif discovery and causality analysis focusing on possible applicability to HRI problems. Detailed explanations of all the algorithms involved are provided with open-source implementations in MATLAB enabling the reader to experiment with them. Imitation and simulation are the key technologies used to attain social behavior autonomously in the proposed approach. Part two gives the reader a wide overview of research in these areas in psychology, and ethology. Based on this background, the authors discuss approaches to endow robots with the ability to autonomously learn how to be social. Data Mining for Social Robots will be essential reading for graduate students and practitioners interested in social and developmental robotics.
Data Mining in Biomedical Imaging, Signaling, and Systems
by Rajendra Acharya U Sumeet DuaData mining can help pinpoint hidden information in medical data and accurately differentiate pathological from normal data. It can help to extract hidden features from patient groups and disease states and can aid in automated decision making. Data Mining in Biomedical Imaging, Signaling, and Systems provides an in-depth examination of the biomedi
Data Mining in Structural Dynamic Analysis: A Signal Processing Perspective
by Magd Abdel Wahab Yun Lai Zhou Nuno M. M. Maia Linya Liu Elói FigueiredoThis book highlights the applications of data mining technologies in structural dynamic analysis, including structural design, optimization, parameter identification, model updating, damage identification, in civil, mechanical, and aerospace engineering. These engineering applications require precise structural design, fabrication, inspection, and further monitoring to obtain a full life-cycle analysis, and by focusing on data processing, data mining technologies offer another aspect in structural dynamic analysis. Discussing techniques in time/frequency domain, such as Hilbert transforms, wavelet theory, and machine learning for structural dynamic analysis to help in structural monitoring and diagnosis, the book is an essential reference resource for beginners, graduates and industrial professionals in various fields.
Data Mining: 17th Australasian Conference, AusDM 2019, Adelaide, SA, Australia, December 2–5, 2019, Proceedings (Communications in Computer and Information Science #1127)
by Lin Liu Kok-Leong Ong Graham Williams Yanchang Zhao Thuc D. Le Warren H. Jin Sebastien WongThis book constitutes the refereed proceedings of the 17th Australasian Conference on Data Mining, AusDM 2019, held in Adelaide, SA, Australia, in December 2019.The 20 revised full papers presented were carefully reviewed and selected from 56 submissions. The papers are organized in sections on research track, application track, and industry showcase.
Data Mining: 20th Australasian Conference, AusDM 2022, Western Sydney, Australia, December 12–15, 2022, Proceedings (Communications in Computer and Information Science #1741)
by Yee Ling Boo Graham Williams Yun Sing Koh Yanchang Zhao Simeon Simoff Laurence A. F. Park Heitor Murilo Gomes Maryam DoborjehThis book constitutes the refereed proceedings of the 20th Australasian Conference on Data Mining, AusDM 2022, held in Western Sydney, Australia, during December 12–15, 2022. The 17 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as research track and application track.
Data Mining: Concepts, Methods and Applications in Management and Engineering Design (Decision Engineering)
by Jiafu Tang Yong Yin Ikou Kaku Jianming ZhuData Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.
Data Mining: Practical Machine Learning Tools And Techniques
by Eibe Frank Mark A. Hall Ian H. Witten Christopher J. PalestroData Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches. <P><P> Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Data Mining: Theories, Algorithms, and Examples (Human Factors And Ergonomics Ser.)
by Nong YeNew technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various dat
Data Modelling and Analytics for the Internet of Medical Things
by Rajiv Pandey Raymond Chiong Pratibha MauryaThe emergence of the Internet of Medical Things (IoMT) is transforming the management of diseases, improving diseases diagnosis and treatment methods, and reducing healthcare costs and errors. This book covers all the essential aspects of IoMT in one place, providing readers with a comprehensive grasp of IoMT and related technologies. Data Modelling and Analytics for the Internet of Medical Things integrates the architectural, conceptual, and technological aspects of IoMT, discussing in detail the IoMT, connected smart medical devices, and their applications to improve health outcomes. It explores various methodologies and solutions for medical data analytics in healthcare systems using machine learning and deep learning approaches, as well as exploring how technologies such as blockchain and cloud computing can further enhance data analytics in the e-health domain. Prevalent IoMT case studies and applications are also discussed. This book is suitable for scientists, design engineers, system integrators, and researchers in the field of IoMT. It will also be of interest to postgraduate students in computer science focusing on healthcare applications and a supplementary reading for IoMT courses.
Data Plane Development Kit (DPDK): A Software Optimization Guide to the User Space-Based Network Applications
by Heqing ZhuThis book brings together the insights and practical experience of some of the most experienced Data Plane Development Kit (DPDK) technical experts, detailing the trend of DPDK, data packet processing, hardware acceleration, packet processing and virtualization, as well as the practical application of DPDK in the fields of SDN, NFV, and network storage. The book also devotes many chunks to exploring various core software algorithms, the advanced optimization methods adopted in DPDK, detailed practical experience, and the guides on how to use DPDK.
Data Privacy During Pandemics: A Scorecard Approach for Evaluating the Privacy Implications of COVID-19 Mobile Phone Surveillance Programs
by Ricardo Sanchez Benjamin Boudreaux Matthew A. DeNardo Sarah W. Denton Katie Feistel Hardika DayalaniAs part of the response to the COVID-19 pandemic, governments worldwide have deployed mobile phone surveillance programs to augment public health interventions. However, these programs raise privacy concerns. The authors of this report examine whether two goals can be achieved concurrently: the use of mobile phones as public health surveillance tools to help manage COVID‑19 and future crises, and the protection of privacy and civil liberties.
Data Processing Techniques and Applications for Cyber-Physical Systems (Advances in Intelligent Systems and Computing #1088)
by Neil Yen Chuanchao Huang Yu-Wei ChanThis book covers cutting-edge and advanced research on data processing techniques and applications for Cyber-Physical Systems. Gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019), held in Shanghai, China on November 15–16, 2019, it examines a wide range of topics, including: distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers and professionals alike, while also providing a useful reference guide for newcomers to the field.
Data Protection and Privacy in Healthcare: Research and Innovations
by Ahmed Elngar, Ambika Pawar, and Prathamesh ChuriThe Healthcare industry is one of the largest and rapidly developing industries. Over the last few years, healthcare management is changing from disease centered to patient centered. While on one side the analysis of healthcare data plays an important role in healthcare management, but on the other side the privacy of a patient’s record must be of equal concern. This book uses a research-oriented approach and focuses on privacy-based healthcare tools and technologies. It offers details on privacy laws with real-life case studies and examples, and addresses privacy issues in newer technologies such as Cloud, Big Data, and IoT. It discusses the e-health system and preserving its privacy, and the use of wearable technologies for patient monitoring, data streaming and sharing, and use of data analysis to provide various health services. This book is written for research scholars, academicians working in healthcare and data privacy domains, as well as researchers involved with healthcare law, and those working at facilities in security and privacy domains. Students and industry professionals, as well as medical practitioners might also find this book of interest.
Data Protection for Software Development and IT: A Practical Introduction
by Ralf KneuperThis book introduces data protection, i.e. the protection of individuals from misuse of their personal data. It provides a special focus on the direct impact on software development, e.g. in the form of functional requirements for software systems resulting from data protection. Chapter 1 provides an initial overview of the basic concepts of data protection and its legal foundations. Chapter 2 then delves deeper into the European General Data Protection Regulation (GDPR), covering in particular its basic concepts, terminology and requirements. Next, the specific implementation and interpretation of GDPR requirements in software and IT are dealt with, starting in chapter 3 with the principles of data protection defined in GDPR and the rights of data subjects in chapter 4. Chapter 5 discusses data transfer between organizations, including the relevant constellations (e.g. through various service providers), the legal framework and its practical implementation. Subsequently, chapter 6 changes the view from individual regulations and their implementation to technical and organizational design of data protection, including its embedding in the software life cycle, while chapter 7 provides an overview of information security and its aspects relevant to data protection. Eventually, chapter 8 deals with data protection for organizations as they are data subjects themselves. The appendices contain the most important excerpts from the Charter of Fundamental Rights of the EU and GDPR in this context, a collection of links to relevant laws and supervisory authorities, as well as a glossary of the most important terms used. The book’s target groups include software developers, IT consultants, requirements analysts, IT operations personnel and project managers in IT projects, but also data protection managers and data protection officers in the context of software development and IT.
Data Protection in the Internet (Ius Comparatum - Global Studies in Comparative Law #38)
by Dário Moura Vicente Sofia de Vasconcelos CasimiroThis book identifies and explains the different national approaches to data protection – the legal regulation of the collection, storage, transmission and use of information concerning identified or identifiable individuals – and determines the extent to which they could be harmonised in the foreseeable future. In recent years, data protection has become a major concern in many countries, as well as at supranational and international levels. In fact, the emergence of computing technologies that allow lower-cost processing of increasing amounts of information, associated with the advent and exponential use of the Internet and other communication networks and the widespread liberalization of the trans-border flow of information have enabled the large-scale collection and processing of personal data, not only for scientific or commercial uses, but also for political uses. A growing number of governmental and private organizations now possess and use data processing in order to determine, predict and influence individual behavior in all fields of human activity. This inevitably entails new risks, from the perspective of individual privacy, but also other fundamental rights, such as the right not to be discriminated against, fair competition between commercial enterprises and the proper functioning of democratic institutions. These phenomena have not been ignored from a legal point of view: at the national, supranational and international levels, an increasing number of regulatory instruments – including the European Union’s General Data Protection Regulation applicable as of 25 May 2018 – have been adopted with the purpose of preventing personal data misuse. Nevertheless, distinct national approaches still prevail in this domain, notably those that separate the comprehensive and detailed protective rules adopted in Europe since the 1995 Directive on the processing of personal data from the more fragmented and liberal attitude of American courts and legislators in this respect. In a globalized world, in which personal data can instantly circulate and be used simultaneously in communications networks that are ubiquitous by nature, these different national and regional approaches are a major source of legal conflict.
Data Protection: The Wake of AI and Machine Learning
by Dushantha Nalin K. Jayakody Chaminda Hewage Lasith YasakethuThis book provides a thorough and unique overview of the challenges, opportunities and solutions related with data protection in the age of AI and ML technologies. It investigates the interface of data protection and new technologies, emphasising the growing need to safeguard personal and confidential data from unauthorised access and change. The authors emphasize the crucial need of strong data protection regulations, focusing on the consequences of AI and ML breakthroughs for privacy and individual rights. This book emphasizes the multifarious aspect of data protection, which goes beyond technological solutions to include ethical, legislative and societal factors. This book explores into the complexity of data protection in the age of AI and ML. It investigates how massive volumes of personal and sensitive data are utilized to train and develop AI models, demanding novel privacy-preserving strategies such as anonymization, differential privacy and federated learning. The duties and responsibilities of engineers, policy makers and ethicists in minimizing algorithmic bias and ensuring ethical AI use are carefully defined. Key developments, such as the influence of the European Union's General Data Protection Regulation (GDPR) and the EU AI Act on data protection procedures, are reviewed critically. This investigation focusses not only on the tactics used, but also on the problems and successes in creating a secure and ethical AI ecosystem. This book provides a comprehensive overview of the efforts to integrate data protection into AI innovation, including valuable perspectives on the effectiveness of these measures and the ongoing adjustments required to address the fluid nature of privacy concerns. This book is a helpful resource for upper-undergraduate and graduate computer science students, as well as others interested in cybersecurity and data protection. Researchers in AI, ML, and data privacy as well as data protection officers, politicians, lawmakers and decision-makers will find this book useful as a reference.