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| data mapping source to target: Data Mapping for Data Warehouse Design Qamar Shahbaz, 2015-12-08 Data mapping in a data warehouse is the process of creating a link between two distinct data models' (source and target) tables/attributes. Data mapping is required at many stages of DW life-cycle to help save processor overhead; every stage has its own unique requirements and challenges. Therefore, many data warehouse professionals want to learn data mapping in order to move from an ETL (extract, transform, and load data between databases) developer to a data modeler role. Data Mapping for Data Warehouse Design provides basic and advanced knowledge about business intelligence and data warehouse concepts including real life scenarios that apply the standard techniques to projects across various domains. After reading this book, readers will understand the importance of data mapping across the data warehouse life cycle. - Covers all stages of data warehousing and the role of data mapping in each - Includes a data mapping strategy and techniques that can be applied to many situations - Based on the author's years of real-world experience designing solutions |
| data mapping source to target: MEDINFO 2023 — The Future Is Accessible J. Bichel-Findlay, P. Otero, P. Scott, 2024-04-02 Science-fiction author William Gibson is famously quoted as saying, “The future is already here – it's just not very evenly distributed.” During the Covid pandemic, telehealth and remote monitoring were elevated from interesting innovations to essential tools in many healthcare systems, but not all countries had the infrastructure necessary to pivot quickly, amply demonstrating the negative consequences of the digital divide. This book presents the proceedings of MedInfo 2023, the 19th World Congress on Medical and Health Informatics, held from 8 – 12 July 2023 in Sydney, Australia. This series of biennial conferences provides a platform for the discussion of applied approaches to data, information, knowledge, and wisdom in health and wellness. The theme and title of MedInfo 2023 was The Future is Accessible, but the digital divide is a major concern for health and care-informatics professionals, whether because of global economic disparities, digital literacy gaps, or limited access to reliable information about health. A total of 935 submissions were received for the conference, of which 228 full papers, 43 student papers and 117 posters were accepted following a thorough peer-review process involving 279 reviewers. Topics covered include: information and knowledge management; quality, safety and outcomes; health data science; human, organizational and social aspects; and global health informatics. Significant advances in artificial intelligence, machine learning, augmented reality, virtual reality, and genomics hold great hope for future healthcare planning, delivery, management, education, evaluation, and research, and this book will be of interest to all those working to not only exploit the benefits of these technologies, but also to identify ways to overcome their associated challenges. |
| data mapping source to target: NBS Special Publication , 1981 |
| data mapping source to target: Deep Learning for the Earth Sciences Gustau Camps-Valls, Devis Tuia, Xiao Xiang Zhu, Markus Reichstein, 2021-08-16 DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists. |
| data mapping source to target: Database and Expert Systems Applications Sourav S. Bhowmick, Josef Küng, Roland Wagner, 2008-08-18 This book constitutes the refereed proceedings of the 19th International Conference on Database and Expert Systems Applications, DEXA 2008, held in Turin, Italy, in September 2008. The 74 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 208 submissions. The papers are organized in topical sections on data privacy; temporal, spatial and high dimensional databases; semantic Web and ontologies; query processing; Web and information retrieval; mobile data and information; data and information streams; data mining algorithms; multimedia databases; data mining systems, data warehousing, OLAP; data and information semantics; XML databases; applications of database, information, and decision support systems; and schema, process and knowledge modelling and evolution. |
| data mapping source to target: Semantic Web and Peer-to-Peer Steffen Staab, Heiner Stuckenschmidt, 2005-12-02 Just like the industrial society of the last century depended on natural resources, today’s society depends on information and its exchange. Staab and Stuckenschmidt structured the selected contributions into four parts: Part I, Data Storage and Access, prepares the semantic foundation, i.e. data modelling and querying in a flexible and yet scalable manner. These foundations allow for dealing with the organization of information at the individual peers. Part II, Querying the Network, considers the routing of queries, as well as continuous queries and personalized queries under the conditions of the permanently changing topological structure of a peer-to-peer network. Part III, Semantic Integration, deals with the mapping of heterogeneous data representations. Finally Part IV, Methodology and Systems, reports experiences from case studies and sample applications. The overall result is a state-of-the-art description of the potential of Semantic Web and peer-to-peer technologies for information sharing and knowledge management when applied jointly. |
| data mapping source to target: Principles of Distributed Database Systems M. Tamer Özsu, Patrick Valduriez, 2011-02-24 This third edition of a classic textbook can be used to teach at the senior undergraduate and graduate levels. The material concentrates on fundamental theories as well as techniques and algorithms. The advent of the Internet and the World Wide Web, and, more recently, the emergence of cloud computing and streaming data applications, has forced a renewal of interest in distributed and parallel data management, while, at the same time, requiring a rethinking of some of the traditional techniques. This book covers the breadth and depth of this re-emerging field. The coverage consists of two parts. The first part discusses the fundamental principles of distributed data management and includes distribution design, data integration, distributed query processing and optimization, distributed transaction management, and replication. The second part focuses on more advanced topics and includes discussion of parallel database systems, distributed object management, peer-to-peer data management, web data management, data stream systems, and cloud computing. New in this Edition: • New chapters, covering database replication, database integration, multidatabase query processing, peer-to-peer data management, and web data management. • Coverage of emerging topics such as data streams and cloud computing • Extensive revisions and updates based on years of class testing and feedback Ancillary teaching materials are available. |
| data mapping source to target: Nursing Informatics for the Advanced Practice Nurse, Second Edition Susan McBride, PhD, RN-BC, CPHIMS, FAAN, Mari Tietze, PhD, RN, FHIMSS, FAAN, 2018-09-28 A “must have” text for all healthcare professionals practicing in the digital age of healthcare. Nursing Informatics for the Advanced Practice Nurse, Second Edition, delivers a practical array of tools and information to show how advanced practice nurses can maximize patient safety, quality of care, and cost savings through the use of technology. Since the first edition of this text, health information technology has only expanded. With increased capability and complexity, the current technology landscape presents new challenges and opportunities for interprofessional teams. Nurses, who are already trained to use the analytic process to assess, analyze, and intervene, are in a unique position to use this same process to lead teams in addressing healthcare delivery challenges with data. The only informatics text written specifically for advanced practice nurses, Nursing Informatics for the Advanced Practice Nurse, Second Edition, takes an expansive, open, and innovative approach to thinking about technology. Every chapter is highly practical, filled with case studies and exercises that demonstrate how the content presented relates to the contemporary healthcare environment. Where applicable, concepts are aligned with the six domains within the Quality and Safety Education in Nursing (QSEN) approach and are tied to national goals and initiatives. Featuring chapters written by physicians, epidemiologists, engineers, dieticians, and health services researchers, the format of this text reflects its core principle that it takes a team to fully realize the benefit of technology for patients and healthcare consumers. What’s New Several chapters present new material to support teams’ optimization of electronic health records Updated national standards and initiatives Increased focus and new information on usability, interoperability and workflow redesign throughout, based on latest evidence Explores challenges and solutions of electronic clinical quality measures (eCQMs), a major initiative in healthcare informatics; Medicare and Medicaid Services use eCQMs to judge quality of care, and how dynamics change rapidly in today’s environment Key Features Presents national standards and healthcare initiatives Provides in-depth case studies for better understanding of informatics in practice Addresses the DNP Essentials, including II: Organization and system leadership for quality improvement and systems thinking, IV: Core Competency for Informatics, and Interprofessional Collaboration for Improving Patient and Population health outcomes Includes end-of-chapter exercises and questions for students Instructor’s Guide and PowerPoint slides for instructors Aligned with QSEN graduate-level competencies |
| data mapping source to target: Schema Matching and Mapping Zohra Bellahsene, Angela Bonifati, Erhard Rahm, 2011-02-14 Requiring heterogeneous information systems to cooperate and communicate has now become crucial, especially in application areas like e-business, Web-based mash-ups and the life sciences. Such cooperating systems have to automatically and efficiently match, exchange, transform and integrate large data sets from different sources and of different structure in order to enable seamless data exchange and transformation. The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks. The authors describe the state of the art by discussing the latest achievements such as more effective methods for matching data, mapping transformation verification, adaptation to the context and size of the matching and mapping tasks, mapping-driven schema evolution and merging, and mapping evaluation and tuning. The overall result is a coherent, comprehensive picture of the field. With this book, the editors introduce graduate students and advanced professionals to this exciting field. For researchers, they provide an up-to-date source of reference about schema and ontology matching, schema and ontology evolution, and schema merging. |
| data mapping source to target: Data Integration Blueprint and Modeling Anthony David Giordano, 2010-12-27 Making Data Integration Work: How to Systematically Reduce Cost, Improve Quality, and Enhance Effectiveness Today’s enterprises are investing massive resources in data integration. Many possess thousands of point-to-point data integration applications that are costly, undocumented, and difficult to maintain. Data integration now accounts for a major part of the expense and risk of typical data warehousing and business intelligence projects--and, as businesses increasingly rely on analytics, the need for a blueprint for data integration is increasing now more than ever. This book presents the solution: a clear, consistent approach to defining, designing, and building data integration components to reduce cost, simplify management, enhance quality, and improve effectiveness. Leading IBM data management expert Tony Giordano brings together best practices for architecture, design, and methodology, and shows how to do the disciplined work of getting data integration right. Mr. Giordano begins with an overview of the “patterns” of data integration, showing how to build blueprints that smoothly handle both operational and analytic data integration. Next, he walks through the entire project lifecycle, explaining each phase, activity, task, and deliverable through a complete case study. Finally, he shows how to integrate data integration with other information management disciplines, from data governance to metadata. The book’s appendices bring together key principles, detailed models, and a complete data integration glossary. Coverage includes Implementing repeatable, efficient, and well-documented processes for integrating data Lowering costs and improving quality by eliminating unnecessary or duplicative data integrations Managing the high levels of complexity associated with integrating business and technical data Using intuitive graphical design techniques for more effective process and data integration modeling Building end-to-end data integration applications that bring together many complex data sources |
| data mapping source to target: The Key to Successful Data Migration Rajender Kumar, 2023-04-15 Are You Engaged in Data Migration Project? Are you tired of dealing with data migration failures, costly downtime, and lost productivity? Do you want to ensure a smooth and successful transition? Want to find ways to mitigate risks, streamline processes and maximize the benefits of data migration? This book provides a comprehensive guide to pre-migration activities which will arm you with knowledge and tools for an effortless transition. With guidance from experienced data migration professionals, this book takes an approachable, hands-on approach to pre-migration activities by offering strategies and techniques for assessing, cleansing and mapping data sets prior to migration. In this book, you will learn: · Learn to define your project scope and objectives to meet the needs of your organization, while simultaneously understanding how important assessing data complexity and using quality metrics can be for making informed decisions. · How to create an effective communication plan to keep all stakeholders updated throughout the migration process · Why it is crucial for organizations to conduct readiness assessments prior to embarking on migration · Automated data mapping tools offer advantages that speed up migration by streamlining processes. Furthermore, using such tools helps mitigate risks associated with data migration while assuring data security during this process. · And much more! This book serves as not only a comprehensive guide to pre-migration activities but also as an evidence-based case study of their successful implementation. But don't just take our word for it. Here's what readers are saying: This book is a game-changer. It helped me navigate through the complexities of data migration and avoid costly mistakes. - John D., IT Manager The practical tips and real-world examples in this book gave me the confidence to take on our data migration project with ease. - Sarah M., Business Analyst No matter what stage of data migration you are at or the type of business leader undertaking the project, The Key to Successful Data Migration: Pre-Migration Activities is your go-to resource for ensuring a smooth and successful migration experience. So don't delay! Start reading now and discover the secrets to unlocking all the potential of your data migration project! |
| data mapping source to target: Semantic Services, Interoperability and Web Applications: Emerging Concepts Sheth, Amit, 2011-06-30 This book offers suggestions, solutions, and recommendations for new and emerging research in Semantic Web technology, focusing broadly on methods and techniques for making the Web more useful and meaningful--Provided by publisher. |
| data mapping source to target: Information Systems Reengineering, Integration and Normalization Joseph S. P. Fong, Kenneth Wong Ting Yan, 2022-01-01 Database technology is an important subject in Computer Science. Every large company and nation needs a database to store information. The technology has evolved from file systems in the 60’s, to Hierarchical and Network databases in the 70’s, to relational databases in the 80’s, object-oriented databases in the 90’s, and to XML documents and NoSQL today. As a result, there is a need to reengineer and update old databases into new databases. This book presents solutions for this task. In this fourth edition, Chapter 9 - Heterogeneous Database Connectivity (HDBC) offers a database gateway platform for companies to communicate with each other not only with their data, but also via their database. The ability of sharing a database can contribute to the applications of Big Data and surveys for decision support systems. The HDBC gateway solution collects input from the database, transfers the data into its middleware storage, converts it into a common data format such as XML documents, and then distributes them to the users. HDBC transforms the common data into the target database to meet the user’s requirements, acting like a voltage transformer hub. The voltage transformer converts the voltage to a voltage required by the users. Similarly, HDBC transforms the database to the target database required by the users. This book covers reengineering for data conversion, integration for combining databases and merging databases and expert system rules, normalization for eliminating duplicate data from the database, and above all, HDBC connects all legacy databases to one target database for the users. The authors provide a forum for readers to ask questions and the answers are given by the authors and the other readers on the Internet. |
| data mapping source to target: IBM FlashSystem Best Practices and Performance Guidelines for IBM Spectrum Virtualize Version 8.4.2 Antonio Rainero, Carlton Beatty, David Green, Hartmut Lonzer, Jonathan Wilkie, Kendall Williams, Konrad Trojok, Mandy Stevens, Nezih Boyacıoglu, Nils Olsson, Renato Santos, Rene Oehme, Sergey Kubin, Thales Noivo Ferreira, Uwe Schreiber, Vasfi Gucer, IBM Redbooks, 2022-02-02 This IBM® Redbooks® publication captures several of the preferred practices and describes the performance gains that can be achieved by implementing the IBM FlashSystem® products that are powered by IBM Spectrum® Virtualize Version 8.4.2. These practices are based on field experience. This book highlights configuration guidelines and preferred practices for the storage area network (SAN) topology, clustered system, back-end storage, storage pools and managed disks, volumes, Remote Copy services, and hosts. It explains how you can optimize disk performance with the IBM System Storage Easy Tier® function. It also provides preferred practices for monitoring, maintaining, and troubleshooting. This book is intended for experienced storage, SAN, IBM FlashSystem, SAN Volume Controller, and IBM Storwize® administrators and technicians. Understanding this book requires advanced knowledge of these environments. |
| data mapping source to target: Relational and XML Data Exchange Marcelo Arenas, Pablo Barcelo, Leonid Libkin, Filip Murlak, 2022-05-31 Data exchange is the problem of finding an instance of a target schema, given an instance of a source schema and a specification of the relationship between the source and the target. Such a target instance should correctly represent information from the source instance under the constraints imposed by the target schema, and it should allow one to evaluate queries on the target instance in a way that is semantically consistent with the source data. Data exchange is an old problem that re-emerged as an active research topic recently, due to the increased need for exchange of data in various formats, often in e-business applications. In this lecture, we give an overview of the basic concepts of data exchange in both relational and XML contexts. We give examples of data exchange problems, and we introduce the main tasks that need to addressed. We then discuss relational data exchange, concentrating on issues such as relational schema mappings, materializing target instances (including canonical solutions and cores), query answering, and query rewriting. After that, we discuss metadata management, i.e., handling schema mappings themselves. We pay particular attention to operations on schema mappings, such as composition and inverse. Finally, we describe both data exchange and metadata management in the context of XML. We use mappings based on transforming tree patterns, and we show that they lead to a host of new problems that did not arise in the relational case, but they need to be addressed for XML. These include consistency issues for mappings and schemas, as well as imposing tighter restrictions on mappings and queries to achieve tractable query answering in data exchange. Table of Contents: Overview / Relational Mappings and Data Exchange / Metadata Management / XML Mappings and Data Exchange |
| data mapping source to target: CAA2016: Oceans of Data Mieko Matsumoto, Espen Uleberg, 2018-12-31 A selection of 50 papers presented at CAA2016. Papers are grouped under the following headings: Ontologies and Standards; Field and Laboratory Data Recording and Analysis; Archaeological Information Systems; GIS and Spatial Analysis; 3D and Visualisation; Complex Systems Simulation; Teaching Archaeology in the Digital Age. |
| data mapping source to target: IBM System Storage SAN Volume Controller and Storwize V7000 Replication Family Services Jon Tate, Rafael Vilela Dias, Ivaylo Dikanarov, Jim Kelly, Peter Mescher, IBM Redbooks, 2017-02-16 This IBM® Redbooks® publication describes the new features that have been added with the release of the IBM System Storage® SAN Volume Controller (SVC) and IBM System Storage Storwize® V7000 6.4.0 code, including Replication Family Services. Replication Family Services refers to the various copy services available on the SVC and Storwize V7000 including IBM FlashCopy®, Metro Mirror and Global Mirror, Global Mirror with Change Volumes, Volume Mirroring, and Stretched Cluster Volume Mirroring. The details behind the theory and practice of these services are examined, and SAN design suggestions and troubleshooting tips are provided. Planning requirements, automating copy services processed, and fabric design are explained. Multiple examples including implementation and server integration are included, along with a discussion of software solutions and services that are based on Replication Family Services. This book is intended for use by pre-sales and post-sales support, and storage administrators. Readers are expected to have an advanced knowledge of the SVC, Storwize V7000, and the SAN environment. The following publications are useful resources that provide background information: Implementing the IBM System Storage SAN Volume Controller V6.3, SG24-7933 Implementing the IBM Storwize V7000 V6.3, SG24-7938 IBM SAN Volume Controller and Brocade Disaster Recovery Solutions for VMware, REDP-4626 IBM System Storage SAN Volume Controller Upgrade Path from Version 4.3.1 to 6.1, REDP-4716 Real-time Compression in SAN Volume Controller and Storwize V7000, REDP-4859 SAN Volume Controller: Best Practices and Performance Guidelines, SG24-7521 Implementing the Storwize V7000 and the IBM System Storage SAN32B-E4 Encryption Switch, SG24-7977 |
| data mapping source to target: AutomationML Rainer Drath, 2021-07-19 This book provides a comprehensive in-depth look into the practical application of AutomationML Edition 2 from an industrial perspective. It is a cookbook for advanced users and describes re-usable pattern solutions for a variety of industrial applications and how to implement it in software. Just to name some: AutomationML modelling of AAS, MTP, SCD, OPC UA, Automation Components, Automation Projects, drive configurations, requirement models, communication systems, electrical interfaces and cables, or semantic integration aspects as eClass integration or handling of semantic heterogeneity. This book guides through the universe of AutomationML from industrial perspective. It is written by AutomationML experts that have industrially implemented AutomationML in pattern solutions for a large variety of applications. This book is structured into three major parts. • Part I: software implementation for developers • Part II: re-usable industrial pattern solutions and domain models • Part III: outlook into future AutomationML applications Additional material to the book and more information about AutomationML on the website: https://www.automationml.org/about-automationml/publications/amlbook/ |
| data mapping source to target: Altova® MapForce® 2013 User & Reference Manual , |
| data mapping source to target: New Methods, Algorithms, and Software for Rapid Mapping of Tree Positions in Coordinate Forest Plots A. D. Wilson, 2000 |
| data mapping source to target: Learn Informatica in 24 Hours Alex Nordeen, 2020-10-31 This is a practical step by step hand-on guide to learn and master Informatica. Informatica is widely used ETL tool and provided end to end data integration and management solution. This book introduces Informatica in detail. It provides a detailed step by step installation tutorial of Informatica. It teaches various activities like data cleansing, data profiling, transforming and scheduling the workflows from source to target in simple steps, etc. Here is what you will learn – Chapter 1: Introduction to Informatica Chapter 2: Informatica Architecture Tutorial Chapter 3: How to Download & Install Informatica PowerCenter Chapter 4: How to Configure Client and Repository in Informatica Chapter 5: Source Analyzer and Target Designer in Informatica Chapter 6: Mappings in Informatica: Create, Components, Parameter, Variable Chapter 7: Workflow in Informatica: Create, Task, Parameter, Reusable, Manager Chapter 8: Workflow Monitor in Informatica: Task & Gantt Chart View Examples Chapter 9: Debugger in Informatica: Session, Breakpoint, Verbose Data & Mapping Chapter 10: Session Properties in Informatica Chapter 11: Introduction to Transformations in Informatica and Filter Transformation Chapter 12: Source Qualifier Transformation in Informatica with EXAMPLE Chapter 13: Aggregator Transformation in Informatica with Example Chapter 14: Router Transformation in Informatica with EXAMPLE Chapter 15: Joiner Transformation in Informatica with EXAMPLE Chapter 16: Rank Transformation in Informatica with EXAMPLE Chapter 17: Sequence Transformation in Informatica with EXAMPLE Chapter 18: Transaction Control Transformation in Informatica with EXAMPLE Chapter 19: Lookup Transformation in Informatica & Re-usable Transformation Example Chapter 20: Normalizer Transformation in Informatica with EXAMPLE Chapter 21: Performance Tuning in Informatica ★★★Download Today ~ Free to Read for Kindle Unlimited Subscribers!★★★ |
| data mapping source to target: Data Science Quick Reference Manual – Methodological Aspects, Data Acquisition, Management and Cleaning Mario A. B. Capurso, This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. First of a series of books, it covers methodological aspects, data acquisition, management and cleaning. It describes the CRISP DM methodology, the working phases, the success criteria, the languages and the environments that can be used, the application libraries. Since this book uses Orange for the application aspects, its installation and widgets are described. Dealing with data acquisition, the book describes data sources, the acceleration techniques, the discretization methods, the security standards, the types and representations of the data, the techniques for managing corpus of texts such as bag-of-words, word-count , TF-IDF, n-grams, lexical analysis, syntactic analysis, semantic analysis, stop word filtering, stemming, techniques for representing and processing images, sampling, filtering, web scraping techniques. Examples are given in Orange. Data quality dimensions are analysed, and then the book considers algorithms for entity identification, truth discovery, rule-based cleaning, missing and repeated value handling, categorical value encoding, outlier cleaning, and errors, inconsistency management, scaling, integration of data from various sources and classification of open sources, application scenarios and the use of databases, datawarehouses, data lakes and mediators, data schema mapping and the role of RDF, OWL and SPARQL, transformations. Examples are given in Orange. The book is accompanied by supporting material and it is possible to download the project samples in Orange and sample data. |
| data mapping source to target: Metadata Management with IBM InfoSphere Information Server Wei-Dong Zhu, Tuvia Alon, Gregory Arkus, Randy Duran, Marc Haber, Robert Liebke, Frank Morreale Jr., Itzhak Roth, Alan Sumano, IBM Redbooks, 2011-10-18 What do you know about your data? And how do you know what you know about your data? Information governance initiatives address corporate concerns about the quality and reliability of information in planning and decision-making processes. Metadata management refers to the tools, processes, and environment that are provided so that organizations can reliably and easily share, locate, and retrieve information from these systems. Enterprise-wide information integration projects integrate data from these systems to one location to generate required reports and analysis. During this type of implementation process, metadata management must be provided along each step to ensure that the final reports and analysis are from the right data sources, are complete, and have quality. This IBM® Redbooks® publication introduces the information governance initiative and highlights the immediate needs for metadata management. It explains how IBM InfoSphereTM Information Server provides a single unified platform and a collection of product modules and components so that organizations can understand, cleanse, transform, and deliver trustworthy and context-rich information. It describes a typical implementation process. It explains how InfoSphere Information Server provides the functions that are required to implement such a solution and, more importantly, to achieve metadata management. This book is for business leaders and IT architects with an overview of metadata management in information integration solution space. It also provides key technical details that IT professionals can use in a solution planning, design, and implementation process. |
| data mapping source to target: Conceptual Modeling - ER 2004 Paolo Atzeni, Wesley Chu, Hongjun Lu, Shuigeng Zhou, Tok Wang Ling, 2005-01-17 On behalf of the Organizing Committee, we would like to welcome you to the proccedings of the 23rd International Conference on Conceptual Modeling (ER 2004). This conference provided an international forum for technical discussion on conceptual modeling of information systems among researchers, developers and users. This was the third time that this conference was held in Asia; the?rst time was in Singapore in 1998 and the second time was in Yokohama, Japan in 2001. China is the third largest nation with the largest population in the world. Shanghai, the largest city in China and a great metropolis, famous in Asia and throughout the world, is therefore a most appropriate location to host this conference. This volume contains papers selected for presentation and includes the two keynote talks by Prof. Hector Garcia-Molina and Prof. Gerhard Weikum, and an invited talk by Dr. Xiao Ji. This volume also contains industrial papers and demo/poster papers. An additional volume contains papers from 6 workshops. The conference also featured three tutorials: (1) Web Change Management andDelta Mining: Opportunities andSolutions, by SanjayMadria, (2)A Survey of Data Quality Issues in Cooperative Information Systems, by Carlo Batini, and (3) Visual SQL - An ER-Based Introduction to Database Programming, by Bernhard Thalheim. |
| data mapping source to target: Nursing Informatics for the Advanced Practice Nurse Susan McBride, PhD, RN-BC, CPHIMS, FAAN, Mari Tietze, PhD, RN, FHIMSS, FAAN, 2015-12-03 Designed specifically for graduate-level nursing informatics courses, this is the first text to focus on using technology with an interprofessional team to improve patient care and safety. It delivers an expansive and innovative approach to devising practical methods of optimizing technology to foster quality of patient care and support population health initiatives. Based on the requirements of the DNP Essential IV Core Competency for Informatics and aligning with federal policy health initiatives, the book describes models of information technology the authors have successfully used in health IT, as well as data and analytics used in business, for-profit industry, and not-for-profit health care association settings, which they have adapted for nursing practice in order to foster optimal patient outcomes. The authors espouse a hybrid approach to teaching with a merged competency and concept-based curriculum. With an emphasis on the benefits of an interprofessional team, the book describes the most effective approaches to health care delivery using health information technology. It describes a nursing informatics model that is comprised of three core domains: point-of-care technology, data management and analytics, and patient safety and quality. The book also includes information on point-of-care applications, population health, data management and integrity, and privacy and security. New and emerging technologies explored include genomics, nanotechnology, artificial intelligence, and data mining. Case studies and critical thinking exercises support the concept-based curriculum and facilitate out-of-the-box thinking. Supplemental materials for instructors include PowerPoint slides and a test bank. While targeted primarily for the nursing arena, the text is also of value in medicine, health information management, occupational therapy, and physical therapy. Key Features: Addresses DNP Essential IV Core Competency for Informatics Focuses specifically on using nursing informatics expertise to improve population health, quality, and safety Advocates an interprofessional team approach to optimizing health IT in all practice settings Stimulates critical thinking skills that can by applied to all aspects of IT health care delivery Discusses newest approaches to interprofessional education for IT health care delivery |
| data mapping source to target: Current Trends in Database Technology - EDBT 2006 Torsten Grust, Hagen Höpfner, Arantza Illarramendi, Stefan Jablonski, Marco Mesiti, Sascha Müller, Paula-Lavinia Patranjan, Kai-Uwe Sattler, Myra Spiliopoulou, Jef Wijsen, 2006-10-17 This book constitutes the thoroughly refereed joint post-proceedings of nine workshops held as part of the 10th International Conference on Extending Database Technology, EDBT 2006, held in Munich, Germany in March 2006. The 70 revised full papers presented were selected from numerous submissions during two rounds of reviewing and revision. |
| data mapping source to target: Journal on Data Semantics XIV Stefano Spaccapietra, Lois Delcambre, 2009-11-24 The LNCS Journal on Data Semantics is devoted to the presentation of notable work that, in one way or another, addresses research and development on issues related to data semantics. The scope of the journal ranges from theories supporting the formal definition of semantic content to innovative domain-specific applications of semantic knowledge. The journal addresses researchers and advanced practitioners working on the semantic web, interoperability, mobile information services, data warehousing, knowledge representation and reasoning, conceptual database modeling, ontologies, and artificial intelligence. Volume XIV results from a rigorous selection among 21 full papers received in response to a call for contributions issued in September 2008. |
| data mapping source to target: Advances in Database Technology - EDBT 2006 Yannis Ioannidis, Marc H. Scholl, Joachim W. Schmidt, Florian Matthes, Mike Hatzopoulos, Klemens Boehm, Alfons Kemper, Torsten Grust, Christian Boehm, 2006-03-10 This book constitutes the refereed proceedings of the 10th International Conference on Extending Database Technology, EDBT 2006, held in Munich, Germany, in March 2006. The 60 revised research papers presented together with eight industrial application papers, 20 software demos, and three invited contributions were carefully reviewed and selected from 352 submissions. The papers are organized in topical sections. |
| data mapping source to target: Effective Document and Data Management Bob Wiggins, 2016-04-29 Effective Document and Data Management illustrates the operational and strategic significance of how documents and data are captured, managed and utilized. Without a coherent and consistent approach the efficiency and effectiveness of the organization may be undermined by less poor management and use of its information. The third edition of the book is restructured to take this broader view and to establish an organizational context in which information is management. Along the way Bob Wiggins clarifies the distinction between information management, data management and knowledge management; helps make sense of the concept of an information life cycle to present and describe the processes and techniques of information and data management, storage and retrieval; uses worked examples to illustrate the coordinated application of data and process analysis; and provides guidance on the application of appropriate project management techniques for document and records management projects. The book will benefit a range of organizations and people, from those senior managers who need to develop coherent and consistent business and IT strategies; to information professionals, such as records managers and librarians who will gain an appreciation of the impact of the technology and of how their particular areas of expertise can best be applied; to system designers, developers and implementers and finally to users. The author can be contacted at curabyte@gmail.com for further information. |
| data mapping source to target: Machine Learning and Knowledge Discovery in Databases Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas, 2023-03-16 Chapters “On the Current State of Reproducibility and Reporting of Uncertainty for Aspect-Based SentimentAnalysis” and “Contextualized Graph Embeddings for Adverse Drug Event Detection” are licensed under theterms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). For further details see license information in the chapter. |
| data mapping source to target: Static Analysis Radhia Cousot, Matthieu Martel, 2010-09-13 This book constitutes the refereed proceedings of the 16th International Symposium on Static Analysis, SAS 2010, held in Perpignan, France in September 2010. The conference was co-located with 3 affiliated workshops: NSAD 2010 (Workshop on Numerical and Symbolic Abstract Domains), SASB 2010 (Workshop on Static Analysis and Systems Biology) and TAPAS 2010 (Tools for Automatic Program Analysis). The 22 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 58 submissions. The papers address all aspects of static analysis including abstract domains, bug detection, data flow analysis, logic programming, systems analysis, type inference, cache analysis, flow analysis, verification, abstract testing, compiler optimization and program verification. |
| data mapping source to target: Implementation Guide for IBM Spectrum Virtualize for Public Cloud on Microsoft Azure Version 8.4.3 Andrew Greenfield, Jackson Shea, Katja Kratt, Lars Dill, Leandro Torolho, Nils Olsson, Pankaj Deshpande, Sushil Sharma, Vasfi Gucer, IBM Redbooks, 2022-03-15 IBM® Spectrum Virtualize for Public Cloud is now available on Microsoft Azure. With IBM Spectrum® Virtualize for Public Cloud 8.4.3, users can deploy a highly available two-node cluster running IBM Spectrum Virtualize for Public Cloud on supported Microsoft Azure virtual machines (VMs). This all-inclusive, bring your own license (BYOL) software offering provides the ability to virtualize, optimize, and provision supported Azure Managed Disk to applications that require the performance of block storage in the cloud with the added efficiencies that IBM Spectrum Virtualize for Public Cloud brings to native infrastructure as a service (IaaS) that is provided by Microsoft Azure. This IBM Redbooks publication gives a broad understanding of the IBM Spectrum Virtualize for Public Cloud on Azure architecture. It also provides planning and implementation details of the common use cases for this new product. This book helps storage and networking administrators plan, implement, install, and configure the IBM Spectrum Virtualize for Public Cloud on Azure offering. It also provides valuable troubleshooting tips. |
| data mapping source to target: The Discipline of Data Jerald Savin, 2023-07-06 Pulling aside the curtain of ‘Big Data’ buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals — how data is generated, organized, stored, evaluated, and maintained — has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as well as business analysts at all career stages and data analytics students. |
| data mapping source to target: Advances in GIScience Monika Sester, Lars Bernard, Volker Paelke, 2009-04-22 The Association of Geographic Information Laboratories for Europe (AGILE) was established in early 1998 to promote academic teaching and research on GIS at the European level. Since then, the annual AGILE c- ference has gradually become the leading GIScience conference in Europe and provides a multidisciplinary forum for scientific knowledge prod- tion and dissemination. GIScience addresses the understanding and automatic processing of geospatial information in its full breadth. While geo-objects can be represented either as vector data or in raster formats these representations have also guided the research in different disciplines, with GIS researchers concentrating on vector data while research in photogrammetry and c- puter vision focused on (geospatial) raster data. Although there have - ways been small but fine sessions addressing photogrammetry and image analysis at past AGILE conferences, these topics typically played only a minor role. Thus to broaden the domain of topics the AGILE 2009 con- rence it is jointly organized with a Workshop of the International Society of Photogrammetry and Remote Sensing (ISPRS), dedicated to High Re- lution Satellite Imagery, organized by Prof. Christian Heipke of the Le- niz Universität Hannover. This collocation provides opportunities to explore commonalities - tween research communities and to ease exchange between participants to develop or deepen mutual understanding. We hope that this approach enables researchers from the different communities to identify common - terests and research methods and thus provides a basis for possible future cooperations. |
| data mapping source to target: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data mapping source to target: Data Exploration Using Example-Based Methods Matteo Lissandrini, Davide Mottin, Themis Palpanas, Yannis Velegrakis, 2022-06-01 Data usually comes in a plethora of formats and dimensions, rendering the exploration and information extraction processes challenging. Thus, being able to perform exploratory analyses in the data with the intent of having an immediate glimpse on some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicate declarative languages (such as SQL) and mechanisms, and at the same time retain the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or the analyst, circumvents query languages by using examples as input. An example is a representative of the intended results, or in other words, an item from the result set. Example-based methods exploit inherent characteristics of the data to infer the results that the user has in mind, but may not able to (easily) express. They can be useful in cases where a user is looking for information in an unfamiliar dataset, when the task is particularly challenging like finding duplicate items, or simply when they are exploring the data. In this book, we present an excursus over the main methods for exploratory analysis, with a particular focus on example-based methods. We show how that different data types require different techniques, and present algorithms that are specifically designed for relational, textual, and graph data. The book presents also the challenges and the new frontiers of machine learning in online settings which recently attracted the attention of the database community. The lecture concludes with a vision for further research and applications in this area. |
| data mapping source to target: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications Wang, John, 2008-05-31 In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data. |
| data mapping source to target: Information Systems A. Sernadas, Janis Bubenko, A. Olivé, 1985 This book contains papers on the theoretical and formal aspects of information systems. The fifteen papers address two main problems in the area: consolidation of the underlying concepts and identification of suitable formal tools for information system development. Several new modeling abstraction mechanisms and languages are presented and discussed, as well as logical formalisms for correctness analysis of specifications. |
| data mapping source to target: Essential Business Process Modeling Michael Havey, 2005-08-18 Ten years ago, groupware bundled with email and calendar applications helped track the flow of work from person to person within an organization. Workflow in today's enterprise means more monitoring and orchestrating massive systems. A new technology called Business Process Management, or BPM, helps software architects and developers design, code, run, administer, and monitor complex network-based business processes BPM replaces those sketchy flowchart diagrams that business analysts draw on whiteboards with a precise model that uses standard graphical and XML representations, and an architecture that allows it converse with other services, systems, and users. Sound complicated? It is. But it's downright frustrating when you have to search the Web for every little piece of information vital to the process. Essential Business Process Modeling gathers all the concepts, design, architecture, and standard specifications of BPM into one concise book, and offers hands-on examples that illustrate BPM's approach to process notation, execution, administration and monitoring. Author Mike Havey demonstrates standard ways to code rigorous processes that are centerpieces of a service-oriented architecture (SOA), which defines how networks interact so that one can perform a service for the other. His book also shows how BPM complements enterprise application integration (EAI), a method for moving from older applications to new ones, and Enterprise Service BUS for integrating different web services, messaging, and XML technologies into a single network. BPM, he says, is to this collection of services what a conductor is to musicians in an orchestra: it coordinates their actions in the performance of a larger composition. Essential Business Process Modeling teaches you how to develop examples of process-oriented applications using free tools that can be run on an average PC or laptop. You'll also learn about BPM design patterns and best practices, as well as some underlying theory. The best way to monitor processes within an enterprise is with BPM, and the best way to navigate BPM is with this valuable book. |
| data mapping source to target: Implementing the IBM FlashSystem 5010 and FlashSystem 5030 with IBM Spectrum Virtualize V8.3.1 Jack Armstrong, Tiago Bastos, Pawel Brodacki, Markus Döllinger, Jon Herd, Sergey Kubin, Carsten Larsen, Hartmut Lonzer, Jon Tate, IBM Redbooks, 2020-10-28 Organizations of all sizes face the challenge of managing massive volumes of increasingly valuable data. But storing this data can be costly, and extracting value from the data is becoming more difficult. IT organizations have limited resources, but must stay responsive to dynamic environments and act quickly to consolidate, simplify, and optimize their IT infrastructures. IBM® FlashSystem 5010 and FlashSystem 5030 systems provide a smarter solution that is affordable, easy to use, and self-optimizing, which enables organizations to overcome these storage challenges. The IBM FlashSystem® 5010 and FlashSystem 5030 deliver efficient, entry-level configurations that are designed to meet the needs of small and midsize businesses. Designed to provide organizations with the ability to consolidate and share data at an affordable price, the system offers advanced software capabilities that are found in more expensive systems. This IBM Redbooks® publication is intended for pre-sales and post-sales technical support professionals and storage administrators. It applies to the IBM FlashSystem 5010 and FlashSystem 5030 and IBM Spectrum® Virtualize V8.3.1. This edition applies to IBM Spectrum Virtualize V8.3.1 and the associated hardware and software detailed within. Screen captures that are included within this book might differ from the generally available (GA) version because parts of this book were written with pre-GA code. On February 11, 2020, IBM announced that it was simplifying its portfolio. This book was written by using previous models of the product line before the simplification; however, most of the general principles apply. If you are in any doubt as to their applicability, work with your local IBM representative. |
Utility Network Data Migration: Best Practices - Esri
Fundamentally, the existing source data will need to be transformed from its current state into the target utility network schema (in an asset package format). The specific purpose of this task is …
An Introduction to PyAnsys - PADT, Inc
Feb 1, 2024 · Ansys has a seemless load transfer mechanism which involves simply dragging the Solution cell from one analysis system (the ‘source’ data to be mapped) to the Setup cell of the …
erwin DI Suite Mapping Management Guide - erwin Data …
This section walks you through managing source to target mappings in the Mapping Man-ager. Mapping Manager is the core of erwin Data Intelligence Suite (DI Suite), where you do the …
Planning for and Designing a Data Warehouse: A Hands-On …
In DI Studio, create source and target metadata. Then use the source to target mapping document to build the transformations. Please note for continuity the data used for the …
Data Integration: Schema Mapping - University at Buffalo
Establishing correspondences between elements of the source and target schemas. Generation of assertions (queries) from schema correspondences. matching schemas represented as …
Data Mapping Best Practices (2016 update) - Indian Hills …
This practice brief defines key data mapping concepts and outlines best practices related to the development and use of data maps. Basic Mapping Concepts Mapping features a number of …
A Classification of Schema Mappings and Analysis of …
These queries transform data from the source schema to conform to the target schema. They can be used to materialize data at the target or used as views in a virtually inte- grated system.
Schema Mapping and Data Translation - uni-mannheim.de
Goal: Translate data from a set of source schemata into a given target schema. 1. Find Correspondences. 2. Interpretation. 3. Query Generation. Goal: Create a new integrated …
Transfer Learning by Mapping with Minimal Target Data
In contrast to our previous work, here we present an ap-proach to mapping source knowledge when minimal target-domain data is available. In particular, our approach addresses the single …
The Process of Data Mapping for Data Integration Projects
Source to target mappings describe how one or more attributes in source data sets are related to one or more attributes in a target data set.
Designing and Refining Schema Mappings via Data Examples
In this paper, we present an alternative paradigm and develop a novel system for designing and refining schema mappings interac-tively via data examples. The intended users of our system …
A Design Technique: Data Integration Modeling
• Nonvalue add analysis—Capturing source-to-target mappings with transformation requirements contains valuable navigational metadata that can be used for data lineage analysis.
SDTM MAPPING AND DATA CONVERSION USING ML/NLP
• Mapping of non-standard raw data to SDTM is manual and time consuming. • Mapping is repetitive process. • Endless mapping and multiple mapping decisions for same scenario. • …
A Source-to-Target Constraint rewriting for Direct Mapping …
W3C recommendation [2], and present a source-to-target semantics preserving rewriting of constraints in an SQL database schema to equivalent SHACL [8] constraints on the RDF …
Data Mapping and Its Impact on Data Integrity
This thought leadership paper explores the relationship of data mapping and data integrity assurance by providing guidance to avoid adverse outcomes involving the use of maps. …
Schema Mapping and Data Translation - uni-mannheim.de
Goal: Translate data from a set of source schemata into a given target schema. 1. Find Correspondences. 2. Translate Data. Goal: Create a new integrated schema that can …
Schema mappings and data examples - OpenProceedings
Background A key task in data integration or data exchange is the design of a schema mapping between database schemas. A schema mapping is a high-level, declarative specification of …
Mapping of Source and Target Data for Application to …
The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define …
An Agile Approach to Data Mapping and Integration
One of the too-often overlooked areas of integration is the “pre-etl” - Source to Target Mapping (STM) process. We can call it the source to target mapping problem.
Transfer Learning from Minimal Target Data by Mapping …
We present the 2 algorithm that finds an effective mapping of predicates from a source model to the target domain in this setting and thus renders pre-existing knowledge useful to the target task.
Utility Network Data Migration: Best Practices - Esri
Fundamentally, the existing source data will need to be transformed from its current state into the target utility network schema (in an asset package format). The specific purpose of this task is to …
An Introduction to PyAnsys - PADT, Inc
Feb 1, 2024 · Ansys has a seemless load transfer mechanism which involves simply dragging the Solution cell from one analysis system (the ‘source’ data to be mapped) to the Setup cell of the …
erwin DI Suite Mapping Management Guide - erwin Data …
This section walks you through managing source to target mappings in the Mapping Man-ager. Mapping Manager is the core of erwin Data Intelligence Suite (DI Suite), where you do the …
Planning for and Designing a Data Warehouse: A Hands-On …
In DI Studio, create source and target metadata. Then use the source to target mapping document to build the transformations. Please note for continuity the data used for the exercises below is …
Data Integration: Schema Mapping - University at Buffalo
Establishing correspondences between elements of the source and target schemas. Generation of assertions (queries) from schema correspondences. matching schemas represented as labelled …
Data Mapping Best Practices (2016 update) - Indian Hills …
This practice brief defines key data mapping concepts and outlines best practices related to the development and use of data maps. Basic Mapping Concepts Mapping features a number of …
A Classification of Schema Mappings and Analysis of …
These queries transform data from the source schema to conform to the target schema. They can be used to materialize data at the target or used as views in a virtually inte- grated system.
Schema Mapping and Data Translation - uni-mannheim.de
Goal: Translate data from a set of source schemata into a given target schema. 1. Find Correspondences. 2. Interpretation. 3. Query Generation. Goal: Create a new integrated schema …
Transfer Learning by Mapping with Minimal Target Data
In contrast to our previous work, here we present an ap-proach to mapping source knowledge when minimal target-domain data is available. In particular, our approach addresses the single-entity …
The Process of Data Mapping for Data Integration Projects
Source to target mappings describe how one or more attributes in source data sets are related to one or more attributes in a target data set.
Designing and Refining Schema Mappings via Data Examples
In this paper, we present an alternative paradigm and develop a novel system for designing and refining schema mappings interac-tively via data examples. The intended users of our system are …
A Design Technique: Data Integration Modeling
• Nonvalue add analysis—Capturing source-to-target mappings with transformation requirements contains valuable navigational metadata that can be used for data lineage analysis.
SDTM MAPPING AND DATA CONVERSION USING ML/NLP
• Mapping of non-standard raw data to SDTM is manual and time consuming. • Mapping is repetitive process. • Endless mapping and multiple mapping decisions for same scenario. • Require highly …
A Source-to-Target Constraint rewriting for Direct Mapping …
W3C recommendation [2], and present a source-to-target semantics preserving rewriting of constraints in an SQL database schema to equivalent SHACL [8] constraints on the RDF graph. …
Data Mapping and Its Impact on Data Integrity
This thought leadership paper explores the relationship of data mapping and data integrity assurance by providing guidance to avoid adverse outcomes involving the use of maps. …
Schema Mapping and Data Translation - uni-mannheim.de
Goal: Translate data from a set of source schemata into a given target schema. 1. Find Correspondences. 2. Translate Data. Goal: Create a new integrated schema that can represent all …
Schema mappings and data examples - OpenProceedings
Background A key task in data integration or data exchange is the design of a schema mapping between database schemas. A schema mapping is a high-level, declarative specification of the …
Mapping of Source and Target Data for Application to …
The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define …
An Agile Approach to Data Mapping and Integration
One of the too-often overlooked areas of integration is the “pre-etl” - Source to Target Mapping (STM) process. We can call it the source to target mapping problem.
Transfer Learning from Minimal Target Data by Mapping …
We present the 2 algorithm that finds an effective mapping of predicates from a source model to the target domain in this setting and thus renders pre-existing knowledge useful to the target task.
