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| data management platform examples: Intelligent Web Data Management: Software Architectures and Emerging Technologies Kun Ma, Ajith Abraham, Bo Yang, Runyuan Sun, 2016-02-19 This book presents some of the emerging techniques and technologies used to handle Web data management. Authors present novel software architectures and emerging technologies and then validate using experimental data and real world applications. The contents of this book are focused on four popular thematic categories of intelligent Web data management: cloud computing, social networking, monitoring and literature management. The Volume will be a valuable reference to researchers, students and practitioners in the field of Web data management, cloud computing, social networks using advanced intelligence tools. |
| data management platform examples: Practical Guide to Clinical Data Management Susanne Prokscha, 1999-01-31 Clinical data management (CDM) has changed from being an essentially clerical task in the late 1970s and early 1980s to a highly computerized, highly specialized field today. And clinical data manages have had to adapt their data management systems and processes accordingly. Practical Guide to Clinical Data Management steers you through a basic understanding of the role of data management in clinical trials and includes more advanced topics such as CDM systems, SOPs, and quality assurance. This book helps you ensure GCP, manage laboratory data, and deal with the kinds of clinical data that can cause difficulties in database applications. With the tools this book provides, you'll learn how to: Ensure that your DMB system is in compliance with federal regulations Build a strategic data management and databsing plan Track and record CRFs Deal with problem data, adverse event data, and legacy data Manage and store lab data Identify and manage discrepancies Ensure quality control over reports Choose a CDM system that is right for your company Create and implement a system validation plan and process Set up and enforce data collection standards Develop test plans and change control systems This book is your guide to finding the most successful and practical options for effective clinical data management. |
| data management platform examples: Introduction to Data Platforms Anthony David Giordano, 2022-11-03 Digital, cloud, and artificial intelligence (AI) have disrupted how we use data. This disruption has changed the way we need to provision, curate, and publish data for the multiple use cases in today's technology-driven environment. This text will cover how to design, develop, and evolve a data platform for all the uses of enterprise data needed in today's digital organization. This book focuses on explaining what a data platform is, what value it provides, how is it engineered, and how to deploy a data platform and support organization. In this context, Introduction to Data Platforms reviews the current requirements for data in the digital age and quantifies the use cases; discusses the evolution of data over the past twenty years, which is a core driver of the modern data platform; defines what a data platform is and defines the architectural components and layers of a data platform; provides the architectural layers or capabilities of a data platform; reviews cloud- and commercial-software vendors that populate the data-platform space; provides a step-by-step approach to engineering, deploying, supporting, and evolving a data-platform environment; provides a step-by-step approach to migrating legacy data warehouses, data marts, and data lakes/sandboxes to a data platform; and reviews organizational structures for managing data platform environments. |
| data management platform examples: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management platform examples: Clinical Data Management Richard K. Rondel, Sheila A. Varley, Colin F. Webb, 2000-02-03 Extensively revised and updated, with the addition of new chapters and authors, this long-awaited second edition covers all aspects of clinical data management. Giving details of the efficient clinical data management procedures required to satisfy both corporate objectives and quality audits by regulatory authorities, this text is timely and an important contribution to the literature. The volume: * is written by well-known and experienced authors in this area * provides new approaches to major topics in clinical data management * contains new chapters on systems software validation, database design and performance measures. It will be invaluable to anyone in the field within the pharmaceutical industry, and to all biomedical professionals working in clinical research. |
| data management platform examples: Networks and Systems Management Iosif G. Ghetie, 2012-12-06 The deployment of communications networks and distributed computing systems requires the use of open, standards-based, integrated management systems. During the last five years, the overall industry effort to develop, enhance, and integrate man agement systems has crystallized in the concept of management platforms. Manage ment platforms are software systems which provide open, multi vendor, multiprotocol distributed management services. They allow multiple management applications to run over core platform services which constitute the essential part of the management platform framework. This book provides a comprehensive analysis of the features and technical character istics of distributed management platforms by examining both qualitative and quanti tative management capabilities required by each management platform service. The analysis covers the management platform run-time environment, the operational aspects of using management platforms, the development environment, which con sists of software toolkits that are used to build management applications, the imple mentation environment, which deals with testing interoperability aspects of using management platforms, and of course the distributed applications services which plat forms make available to management applications. Finally, the analysis covers the capabilities of several management applications, either generic or specific to devices or resources which run on top of management platforms. |
| data management platform examples: The Art of Maximizing Debt Collections Darryl D'Souza, 2024-04-04 Diving deep into the realm of debt collections, this comprehensive guide, titled (The Art of Maximizing Debt Collections - Digitization, Analytics, AI, Machine Learning, Performance Management), serves as an authoritative handbook on the evolving landscape of collections analytics, automation, and strategic performance measurement. From its compelling introduction, uncovering five processes revolutionizing debt collections, to its culmination in exploring cutting edge applications of AI, machine learning, and robotic assistance in collections, this book is a definitive road map for professionals navigating the intricate world of debt recovery. Spanning fifteen meticulously crafted chapters, each segment is a treasure trove of insights. It begins by elucidating the critical role of collections analytics, unraveling how data management, reporting, and workflow analysis amplify collections strategies. In subsequent chapters, it explores the arsenal of software, tools, and operational reporting mechanisms employed in this domain, enhancing operational efficiencies and agent performance. The book delves into the dynamic realm of collections automation, highlighting the transformative impact of automated systems on debt recovery, while meticulously detailing the top-tier software and tips for selecting optimal automation tools. Moreover, it offers an in-depth exploration of collections performance measurement, unveiling key performance indicators (KPIs) crucial for gauging efficiency. Chapters dedicated to recovery performance, strategy analysis, digitization, and the integration of AI and Machine Learning offer strategic insights into bolstering collections strategies and leveraging technological advancements for enhanced outcomes. Intriguingly it addresses the ethical and legal aspects surrounding the use of robots in basic calling and automated payment promises, providing guidance to navigate these complex territories. The Art of Maximizing Debt Collections - Digitization, Analytics, AI, Machine Learning and Performance Management is an indispensable guide for professionals, analyst and decision makers seeking a comprehensive understanding of collections analytics, automation, and leveraging cutting edge technologies for optimizing Debt Recovery strategies in todays dynamic financial landscape. |
| data management platform examples: Guide to Web Application and Platform Architectures Stefan Jablonski, Ilia Petrov, Christian Meiler, Udo Mayer, 2013-03-09 New concepts and technologies are being introduced continuously for application development in the World-Wide Web. Selecting the right implementation strategies and tools when building a Web application has become a tedious task, requiring in-depth knowledge and significant experience from both software developers and software managers. The mission of this book is to guide the reader through the opaque jungle of Web technologies. Based on their long industrial and academic experience, Stefan Jablonski and his coauthors provide a framework architecture for Web applications which helps choose the best strategy for a given project. The authors classify common technologies and standards like .NET, CORBA, J2EE, DCOM, WSDL and many more with respect to platform, architectural layer, and application package, and guide the reader through a three-phase development process consisting of preparation, design, and technology selection steps. The whole approach is exemplified using a real-world case: the architectural design of an order-entry management system. |
| data management platform examples: COBIT 5: Enabling Information ISACA, 2013-10-10 |
| data management platform examples: Recent Progress in Data Engineering and Internet Technology Ford Lumban Gaol, 2012-08-13 The latest inventions in internet technology influence most of business and daily activities. Internet security, internet data management, web search, data grids, cloud computing, and web-based applications play vital roles, especially in business and industry, as more transactions go online and mobile. Issues related to ubiquitous computing are becoming critical. Internet technology and data engineering should reinforce efficiency and effectiveness of business processes. These technologies should help people make better and more accurate decisions by presenting necessary information and possible consequences for the decisions. Intelligent information systems should help us better understand and manage information with ubiquitous data repository and cloud computing. This book is a compilation of some recent research findings in Internet Technology and Data Engineering. This book provides state-of-the-art accounts in computational algorithms/tools, database management and database technologies, intelligent information systems, data engineering applications, internet security, internet data management, web search, data grids, cloud computing, web-based application, and other related topics. |
| data management platform examples: CAD for Control Systems Linkens, 1993-06-29 This comprehensive collection brings together current information on CAD for control systems including present and future trends in computer-aided design exploring the areas of modeling, simulation, simulation languages, environments, and design techniques. Presenting a systems approach to control d |
| data management platform examples: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management platform examples: Database Management using AI: A Comprehensive Guide A Purushotham Reddy, 2024-10-20 Database Management Using AI: A Comprehensive Guide is a professional yet accessible exploration of how artificial intelligence (AI) is reshaping the world of database management. Designed for database administrators, data scientists, and tech enthusiasts, this book walks readers through the transformative impact of AI on modern data systems. The guide begins with the fundamentals of database management, covering key concepts such as data models, SQL, and the principles of database design. From there, it delves into the powerful role AI plays in optimizing database performance, enhancing security, and automating complex tasks like data retrieval, query optimization, and schema design. The book doesn't stop at theory. It brings AI to life with practical case studies showing how AI-driven database systems are being used in industries such as e-commerce, healthcare, finance, and logistics. These real-world examples demonstrate AI's role in improving efficiency, reducing errors, and driving intelligent decision-making. Key topics covered include: Introduction to Database Systems: Fundamentals of database management, from relational databases to modern NoSQL systems. AI Integration: How AI enhances database performance, automates routine tasks, and strengthens security. Real-World Applications: Case studies from diverse sectors like healthcare, finance, and retail, showcasing the practical impact of AI in database management. Predictive Analytics and Data Mining: How AI tools leverage data to make accurate predictions and uncover trends. Future Trends: Explore cutting-edge innovations like autonomous databases and cloud-based AI solutions that are shaping the future of data management. With its clear explanations and actionable insights, Database Management Using AI equips readers with the knowledge to navigate the fast-evolving landscape of AI-powered databases, making it a must-have resource for those looking to stay ahead in the digital age. |
| data management platform examples: Designing Software Architectures Humberto Cervantes, Rick Kazman, 2016-04-29 Designing Software Architectures will teach you how to design any software architecture in a systematic, predictable, repeatable, and cost-effective way. This book introduces a practical methodology for architecture design that any professional software engineer can use, provides structured methods supported by reusable chunks of design knowledge, and includes rich case studies that demonstrate how to use the methods. Using realistic examples, you’ll master the powerful new version of the proven Attribute-Driven Design (ADD) 3.0 method and will learn how to use it to address key drivers, including quality attributes, such as modifiability, usability, and availability, along with functional requirements and architectural concerns. Drawing on their extensive experience, Humberto Cervantes and Rick Kazman guide you through crafting practical designs that support the full software life cycle, from requirements to maintenance and evolution. You’ll learn how to successfully integrate design in your organizational context, and how to design systems that will be built with agile methods. Comprehensive coverage includes Understanding what architecture design involves, and where it fits in the full software development life cycle Mastering core design concepts, principles, and processes Understanding how to perform the steps of the ADD method Scaling design and analysis up or down, including design for pre-sale processes or lightweight architecture reviews Recognizing and optimizing critical relationships between analysis and design Utilizing proven, reusable design primitives and adapting them to specific problems and contexts Solving design problems in new domains, such as cloud, mobile, or big data |
| data management platform examples: Information Services Today Sandra Hirsh, 2022-03-08 This third edition of Information Services Today: An Introduction demonstrates the ever-changing landscape of information services today and the need to re-evaluate curriculum, competency training, professional development, and lifelong learning in order to stay abreast of current trends and issues, and more significantly, remain competent to address the changing user needs of information communities. Specifically, the Information Services Today: An Introduction: provides a thorough introduction, history, and overall state of the field, explores different types of information communities, the varying information needs within those communities, and the role of equity of access, diversity, inclusion, and social justice in those communities, addresses why information organizations and information and technological literacy are more important today than ever before, discusses how technology has influenced the ways that information professionals provide information resources and services in today’s digital environment, highlights current issues and trends and provides expert insight into emerging challenges, innovations, and opportunities for the future, and identifies career management strategies and leadership opportunities in the information professions. The new edition features chapter updates to address changes in information services, introducing new/updated topics such as emergency/crisis management/community resilience, sustainability, data analysis and visualization, social justice, and equity of access, diversity, and inclusion (EDI). Information Services Today: An Introduction begins with an overview of libraries and their transformation as information and technological hubs within their local and digital communities, as well as trends impacting the information field. Information Services Today: An Introduction covers the various specializations within the field – emphasizing the exciting yet complex roles and opportunities for information professionals in a variety of information environments. With that foundation in place, it presents the fundamentals of information services, delves into management skills needed by information professionals today, and explores emerging issues related to the rapid development of new technologies. Information Services Today: An Introduction addresses how libraries and information centers serve different kinds of communities, highlighting the unique needs of increasingly diverse users. Information Services Today: An Introduction provokes discussion, critical thinking, and interaction to facilitate the learning process. The content and supplemental materials – discussion questions, rich sets of online accessible materials, multimedia webcast interviews featuring authors from this book discussing the trends and issues in their respective areas, and chapter presentation slides for use by instructors – give readers the opportunity to develop a deeper understanding of and engagement with the topics. |
| data management platform examples: Cloud Computing for Science and Engineering Ian Foster, Dennis B. Gannon, 2017-09-29 A guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The emergence of powerful, always-on cloud utilities has transformed how consumers interact with information technology, enabling video streaming, intelligent personal assistants, and the sharing of content. Businesses, too, have benefited from the cloud, outsourcing much of their information technology to cloud services. Science, however, has not fully exploited the advantages of the cloud. Could scientific discovery be accelerated if mundane chores were automated and outsourced to the cloud? Leading computer scientists Ian Foster and Dennis Gannon argue that it can, and in this book offer a guide to cloud computing for students, scientists, and engineers, with advice and many hands-on examples. The book surveys the technology that underpins the cloud, new approaches to technical problems enabled by the cloud, and the concepts required to integrate cloud services into scientific work. It covers managing data in the cloud, and how to program these services; computing in the cloud, from deploying single virtual machines or containers to supporting basic interactive science experiments to gathering clusters of machines to do data analytics; using the cloud as a platform for automating analysis procedures, machine learning, and analyzing streaming data; building your own cloud with open source software; and cloud security. The book is accompanied by a website, Cloud4SciEng.org, that provides a variety of supplementary material, including exercises, lecture slides, and other resources helpful to readers and instructors. |
| data management platform examples: Pro C# 2008 and the .NET 3.5 Platform Andrew Troelsen, 2008-02-22 .NET 3.5 is Microsoft’s largest development software launch since .NET 2.0 and (unlike .NET 3.0) completely replaces all previous .NET versions. A new version of Visual Studio – Visual Studio ‘Orcas’ is being created for the new Framework together with new versions of both the C# and Visual Basic languages. This book deals with this new C# language and provides developers with a complete treatise on the new technology – explaining the importance of all the new features (lambda expressions, LINQ, ASP.NET AJAX, WPF everywhere) and how they integrate into the framework of the previous .NET versions. It is a comprehensively revised and updated version of the author’s previous award-winning titles. |
| data management platform examples: Blockchain for Smart Systems Latesh Malik, Sandhya Arora, Urmila Shrawankar, Vivek Deshpande, 2022-07-06 Blockchain technology has been penetrating every aspect of Information and Communications Technology (ICT), and its use has been growing rapidly in recent years. The interest and development of this technology has primarily been driven by the enormous value growth of cryptocurrencies and large investments of venture capital in blockchain start-ups. Blockchain for Smart Systems: Computing Technologies and Applications is intended to clarify and define, in simple terms, the technology behind blockchain. It provides a deep dive into the core fundamentals of blockchain: hashing algorithm behind each block, distributed technology, smart contracts, and private vs. public blockchain. Features Discusses fundamental theories of practical and sophisticated applications of blockchain technology Includes case studies Discusses the concepts with illustrations, appropriate figures, tables, and simple language This book is primarily aimed at undergraduates, graduates, research scholars, academicians, and industry and technology enthusiasts working in various aspects of blockchain technology. |
| data management platform examples: MAPPING: MAnagement and Processing of Images for Population ImagiNG Michel Dojat, Wiro Niessen, David N. Kennedy, 2017-09-04 Several recent papers underline methodological points that limit the validity of published results in imaging studies in the life sciences and especially the neurosciences (Carp, 2012; Ingre, 2012; Button et al., 2013; Ioannidis, 2014). At least three main points are identified that lead to biased conclusions in research findings: endemic low statistical power and, selective outcome and selective analysis reporting. Because of this, and in view of the lack of replication studies, false discoveries or solutions persist. To overcome the poor reliability of research findings, several actions should be promoted including conducting large cohort studies, data sharing and data reanalysis. The construction of large-scale online databases should be facilitated, as they may contribute to the definition of a “collective mind” (Fox et al., 2014) facilitating open collaborative work or “crowd science” (Franzoni and Sauermann, 2014). Although technology alone cannot change scientists’ practices (Wicherts et al., 2011; Wallis et al., 2013, Poldrack and Gorgolewski 2014; Roche et al. 2014), technical solutions should be identified which support a more “open science” approach. Also, the analysis of the data plays an important role. For the analysis of large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be ensured (MacKenzie-Graham et al., 2008). In population imaging this would mean providing effective tools for data sharing and analysis without increasing the burden on researchers. This subject is the main objective of this research topic (RT), cross-listed between the specialty section “Computer Image Analysis” of Frontiers in ICT and Frontiers in Neuroinformatics. Firstly, it gathers works on innovative solutions for the management of large imaging datasets possibly distributed in various centers. The paper of Danso et al. describes their experience with the integration of neuroimaging data coming from several stroke imaging research projects. They detail how the initial NeuroGrid core metadata schema was gradually extended for capturing all information required for future metaanalysis while ensuring semantic interoperability for future integration with other biomedical ontologies. With a similar preoccupation of interoperability, Shanoir relies on the OntoNeuroLog ontology (Temal et al., 2008; Gibaud et al., 2011; Batrancourt et al., 2015), a semantic model that formally described entities and relations in medical imaging, neuropsychological and behavioral assessment domains. The mechanism of “Study Card” allows to seamlessly populate metadata aligned with the ontology, avoiding fastidious manual entrance and the automatic control of the conformity of imported data with a predefined study protocol. The ambitious objective with the BIOMIST platform is to provide an environment managing the entire cycle of neuroimaging data from acquisition to analysis ensuring full provenance information of any derived data. Interestingly, it is conceived based on the product lifecycle management approach used in industry for managing products (here neuroimaging data) from inception to manufacturing. Shanoir and BIOMIST share in part the same OntoNeuroLog ontology facilitating their interoperability. ArchiMed is a data management system locally integrated for 5 years in a clinical environment. Not restricted to Neuroimaging, ArchiMed deals with multi-modal and multi-organs imaging data with specific considerations for data long-term conservation and confidentiality in accordance with the French legislation. Shanoir and ArchiMed are integrated into FLI-IAM1, the national French IT infrastructure for in vivo imaging. |
| data management platform examples: Advanced Concepts of Information Technology Kashif Qureshi, 2018-12-20 Information technology, which is exclusively designed to store, process, and transmits information, is known as Information Technology.Computers and Information Technology are an indispensable part of any organization. The first edition of Advance concept of Information Technology has been shaped according the needs of current organizational and academic needs This book not only for bachelor’s degree and master’s degree students but also for all those who want to strengthen their knowledge of computers. Furthermore, this book is full to capacity with expert guidance from high-flying IT professionals, in-depth analyses. It presents a detailed functioning of hardware components besides covering the software concepts in detail. An extensive delineate of computer architecture, data representation in the computer, operating systems, database management systems, programming languages, etc. have also been included marvelously in an array .One should use this book to acquire computer literacy in terms of how data is represented in a computer, how hardware devices are integrated to get the desired results, and how the computer works with software and hardware. Features and applications of Information Technology – |
| data management platform examples: Field Trials of Health Interventions Peter G. Smith, Richard H. Morrow, David A. Ross, 2015 This is an open access title available under the terms of a CC BY-NC 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Before new interventions are released into disease control programmes, it is essential that they are carefully evaluated in field trials'. These may be complex and expensive undertakings, requiring the follow-up of hundreds, or thousands, of individuals, often for long periods. Descriptions of the detailed procedures and methods used in the trials that have been conducted have rarely been published. A consequence of this, individuals planning such trials have few guidelines available and little access to knowledge accumulated previously, other than their own. In this manual, practical issues in trial design and conduct are discussed fully and in sufficient detail, that Field Trials of Health Interventions may be used as a toolbox' by field investigators. It has been compiled by an international group of over 30 authors with direct experience in the design, conduct, and analysis of field trials in low and middle income countries and is based on their accumulated knowledge and experience. Available as an open access book via Oxford Medicine Online, this new edition is a comprehensive revision, incorporating the new developments that have taken place in recent years with respect to trials, including seven new chapters on subjects ranging from trial governance, and preliminary studies to pilot testing. |
| data management platform examples: Attribute-based Encryption (ABE) Qi Xia, Jianbin Gao, Isaac Amankona Obiri, Kwame Omono Asamoah, Daniel Adu Worae, 2023-10-03 Attribute-based Encryption (ABE) Enables readers to understand applications of attribute-based encryption schemes in cloud computing with the support of blockchain technology With a focus on blockchain technology, Attribute-based Encryption (ABE) provides insight into the application of attribute-based encryption (ABE) schemes, discussing types of blockchains, fundamentals of blockchain, and how blockchains are constructed. Comprised of 16 chapters, the text provides an overview of the components that go into creating a dual ABE system of encryption proofs within the following: composite bilinear groups, dual pairing vector space framework, matrix pairing framework, framework for matrix pairing, and the application of cryptographic scheme on blockchain. The team of authors discuss the basic construction components of ABE and share the security models, including the selective and semi- adaptive security models, applying these to either prime order or composite order groups. The book also discusses the tools used for converting a composite order ABE scheme to prime order and an adaptive secure ABE scheme based on prime order. In Attribute-based Encryption (ABE), readers can expect to find information on: Mathematical background of ABE, covering group and cyclic group, elliptic curves, curve selection, supersingular curves, ordinary curves, and weil and tate pairing Basic construction components of ABE, covering access structure, monotone Boolean formula, linear secret-sharing scheme, and ordered binary decision diagram Tools for converting composite order ABE schemes to prime order, covering security assumptions and conversion based on vectors for preliminaries, scheme construction, and security proof technique Foundations of blockchain technology, covering blocks, miners, hash functions, and public key cryptography Attribute-based Encryption (ABE) is an essential resource for professionals working in the field of design and cybersecurity who wish to understand how to use blockchain and the ABE scheme to provide fine-grained access control in outsourced data on third-party cloud servers. |
| data management platform examples: The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies Carl F. Hostetter, 1995 |
| data management platform examples: Essentials of Anatomic Pathology Liang Cheng, David G. Bostwick, 2011-03-18 In the 8 years since the publication of the first edition of Essentials of Anatomic Pathology, great strides have been made in our understanding of diseases and neoplastic processes. Many clinically important new histopathologic entities have been described or more fully defined in virtually every organ. Numerous clinically important diagnostic and prognostic markers have entered routine practice. Genetic testing for the early detection of cancers and the molecular classification of diseases has become increasingly important. This is an age of enlightenment in surgical pathology, and the authors of this new volume have captured this sense of excitement herein. This much praised and widely used reference manual on has been extensively revised and expanded to cover the entire field of anatomic pathology. The Third Edition features the incorporation of full-color images in the text with updates of new diagnostic and prognostic information. New classifications and numerous new entities and histologic variants are fully explored. Useful immunostaining biomarkers and emerging molecular targets and relevant molecular findings that have emerged from recent genomic studies are incorporated in each chapter. Written by internationally recognized authorities, the comprehensive, evidence-based practice information is presented in an outline format that is clear and easy to follow. Up-to-date and richly detailed, Essentials of Anatomic Pathology, Third Edition offers both the pathologist-in-training and the practicing pathologist a concise summary of all the critical information needed to recognize, understand and interpret anatomic pathology. |
| data management platform examples: Environmental Software Systems. Frameworks of eEnvironment Jiri Hrebicek, Gerald Schimak, Ralf Denzer, 2011-06-28 This book constitutes the refereed proceedings of the 9th IFIP WG 5.11 International Symposium on Environmental Software Systems, ISESS 2011, held in Brno, Czech Republic, in June 2011. The 68 revised full papers presented together with four invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in the following topical sections: eEnvironment and cross-border services in digital agenda for Europe; environmental information systems and services - infrastructures and platforms; semantics and environment; information tools for global environmental assessment; climate services and environmental tools for urban planning and climate change - applications and services. |
| data management platform examples: Computerworld , 1990-10-22 For more than 40 years, Computerworld has been the leading source of technology news and information for IT influencers worldwide. Computerworld's award-winning Web site (Computerworld.com), twice-monthly publication, focused conference series and custom research form the hub of the world's largest global IT media network. |
| data management platform examples: Earth System Monitor , 1990 |
| data management platform examples: Big Data Analytics Arun K. Somani, Ganesh Chandra Deka, 2017-10-30 The proposed book will discuss various aspects of big data Analytics. It will deliberate upon the tools, technology, applications, use cases and research directions in the field. Chapters would be contributed by researchers, scientist and practitioners from various reputed universities and organizations for the benefit of readers. |
| data management platform examples: Big Data and Cloud Computing for Development Nir Kshetri, Torbjörn Fredriksson, Diana Carolina Rojas Torres, 2017-03-27 This book provides a framework for evaluating big data and cloud computing based on how they evolve to fit users’ needs in developing countries in key areas, such as agriculture and education. The authors discuss how this framework can be utilized by businesses, governments, and consumers to accelerate economic growth and overcome information and communication barriers. By examining the ways in which cloud computing can drive social, economic, and environmental transformation, readers gain a nuanced understanding of the opportunities and challenges these technologies offer. The authors also provide an authoritative and up-to-date account of big data’s diffusion into a wide range of developing economies, such as Brazil and China, illustrating key concepts through in-depth case studies. Special attention is paid to economic development in the context of the new Sustainable Development Goals formulated by the United Nations, introducing readers to the most modern standard of economic evaluation. Students of information management, entrepreneurship, and development, as well as policy makers, researchers, and practitioners, will find Big Data and Cloud Computing for Development an interesting read and a useful reference source. |
| data management platform examples: The Cloud Computing Journey Divit Gupta, 2024-01-05 Elevate your expertise and gain holistic insights into cloud technology with a focus on smoothly transitioning from on-premises to the cloud Key Features Analyze cloud architecture in depth, including different layers, components, and design principles Explore various types of cloud services from AWS, Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure, and more Implement best practices and understand the use of various cloud deployment tools Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionAs the need for digital transformation and remote work surges, so does the demand for cloud computing. However, the complexity of cloud architecture and the abundance of vendors and tools can be overwhelming for businesses. This book addresses the need for skilled professionals capable of designing, building, and managing scalable and resilient cloud systems to navigate the complex landscape of cloud computing through practical tips and strategies. This comprehensive cloud computing guide offers the expertise and best practices for evaluating different cloud vendors and tools. The first part will help you gain a thorough understanding of cloud computing basics before delving deeper into cloud architecture, its design, and implementation. Armed with this expert insight, you'll be able to avoid costly mistakes, ensure that your cloud systems are secure and compliant, and build cloud systems that can adapt and grow with the business. By the end of this book, you’ll be proficient in leveraging different vendors and tools to build robust and secure cloud systems to achieve specific goals and meet business requirements.What you will learn Get to grips with the core concepts of cloud architecture and cost optimization Understand the different cloud deployment and service models Explore various cloud-related tools and technologies Discover cloud migration strategies and best practices Find out who the major cloud vendors are and what they offer Analyze the impact and future of cloud technology Who this book is for The book is for anyone interested in understanding cloud technology, including business leaders and IT professionals seeking insights into the benefits, challenges, and best practices of cloud computing. Those who are just starting to explore cloud technology, as well as those who are already using cloud technology and want to deepen their understanding to optimize usage, will find this resource especially useful. |
| data management platform examples: Strategic Financial Management Muhammad Ali, Leong Choi-Meng, Chin-Hong Puah, Syed Ali Raza, Premagowrie Sivanandan, 2024-10-25 Investigating theoretical frameworks, identifying problems, and discussing implications for managers, entrepreneurs, and policymakers, regulatory changes and compliance challenges are dissected in this book, providing a timely guide for managers to navigate the evolving regulatory landscape. |
| data management platform examples: Secure Data Provenance and Inference Control with Semantic Web Bhavani Thuraisingham, Tyrone Cadenhead, Murat Kantarcioglu, Vaibhav Khadilkar, 2014-08-01 With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing. |
| data management platform examples: Data Governance Dimitrios Sargiotis, |
| data management platform examples: Sensor Technologies for Civil Infrastructures Jerome P. Lynch, Hoon Sohn, Ming L. Wang, 2022-07-19 Sensor Technologies for Civil Infrastructure, Volume 1: Sensing Hardware and Data Collection Methods for Performance Assessment, Second Edition, provides an overview of sensor hardware and its use in data collection. The first chapters provide an introduction to sensing for structural performance assessment and health monitoring, and an overview of commonly used sensors and their data acquisition systems. Further chapters address different types of sensor including piezoelectric transducers, fiber optic sensors, acoustic emission sensors, and electromagnetic sensors, and the use of these sensors for assessing and monitoring civil infrastructures. The new edition now includes chapters on machine learning methods and reliability analysis for structural health monitoring. All chapters have been revised to include the latest advances in materials (such as piezoelectric and mechanoluminescent materials), technologies (such as LIDAR), and applications. - Describes sensing hardware and data collection, covering a variety of sensors including LIDAR - Examines fiber optic systems, acoustic emission, piezoelectric sensors, electromagnetic sensors, terahertz technologies, ultrasonic methods, and radar and millimeter wave technology - Covers strain gauges, micro-electro-mechanical systems (MEMS), multifunctional materials and nanotechnology for sensing, and vision-based sensing and lasers - Includes new chapters on machine learning methods and reliability analysis |
| data management platform examples: ASEAN Space Programs Quentin Verspieren, Maximilien Berthet, Giulio Coral, Shinichi Nakasuka, Hideaki Shiroyama, 2022-01-12 This book presents the first-ever comprehensive analysis of ASEAN space development programs. Written by prominent actors in the region, it goes beyond a mere exposé of the history, current status and future plans of ASEAN space technology development and utilization programs, by analyzing the conditions in which a space program can be initiated in the region. It does so in two ways: on the one hand, it questions the relevance of and motivations behind the inception of space development programs in developing countries, and on the other hand, it focuses on the very specific context of ASEAN (a highly disaster-prone area shaped by unique political alliances with a distinctive geopolitical ecosystem and enormous economic potential, etc.). Last but not least, after having analyzed established and emerging space programs in the region, it provides concrete recommendations for any regional or extra-regional developing nation eager to gain a foothold in space. As such, this book offers a valuable resource for researchers and engineers in the field of space technology, as well as for space agencies and government policymakers. |
| data management platform examples: Modern Big Data Architectures Dominik Ryzko, 2020-03-31 Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning. |
| data management platform examples: Big Data Benchmarking Tilmann Rabl, Kai Sachs, Meikel Poess, Chaitanya Baru, Hans-Arno Jacobson, 2015-06-13 This book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Big Data Benchmarking, WBDB 2014, held in Potsdam, Germany, in August 2014. The 13 papers presented in this book were carefully reviewed and selected from numerous submissions and cover topics such as benchmarks specifications and proposals, Hadoop and MapReduce - in the different context such as virtualization and cloud - as well as in-memory, data generation, and graphs. |
| data management platform examples: Marketing and Sales Automation Uwe Hannig, Uwe Seebacher, 2023-05-02 This book clarifies based on latest findings and research what one needs to know about marketing and sales automation, how to manage projects to implement them, select and implement tools, and what results can be achieved. It also outlines what can be expected in the future such as the automation of corporate communication and Human Resources. The range of topics spans from the creation of a valid data base in the context of applied AI for realizing predictive intelligence and the effects of data regulations such as the European General Data Protection Regulation (GDPR) when addressing customers and prospects to recommendations for selecting and implementing the necessary IT systems. Experts also report on their experiences in regard to Conversion-rate-optimization (CRO) and provide tips and assistance on how to optimize and ensure the highest RoI for marketing and sales automation. A special focus will be placed on the dovetailing of marketing and sales and the management of the customer journey as well as the improvement of the customer experience. |
| data management platform examples: Data Analytics for Smart Cities Amir Alavi, William G. Buttlar, 2018-10-26 The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications. |
| data management platform examples: Marine Hydrocarbon Spill Assessments Oleg Makarynskyy, 2021-08-19 Marine Hydrocarbon Spill Assessments: From Risk of Spill through to Probabilities Estimates describes the methods used for estimating hydrocarbon spill risks and the potential consequences. Throughout the book, mathematical methodologies and algorithms are included to aid the reader in the solving of applied tasks presented. Marine Hydrocarbon Spill Assessments: From Risk of Spill through to Probabilities Estimates provides a fundamental understanding of the oil properties and processes which determine the persistence and impacts of oils in the marine environment. It informs the reader of the current research in hydrocarbon spill assessments, starting from an assessment of a risk of a spill, and moving on to modelling approaches to impact assessments, laboratory toxicity assessments, field impact assessments and response options, and prevention and contingency planning. - Identifies efficient solutions to protect coastal regions from the marine pollution of hydrocarbon spills - Includes case studies examining and analyzing spills, providing lessons to prevent these in the future - Covers the science of oil spills from risk analysis to cleanup and the effects on the environment |
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 …
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 enable a …
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 to …
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 …
