The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform
Wang Jing-chu - One of the best experts on this subject based on the ideXlab platform.
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Digital Campus Data Integration Architecture Analysis
Journal of Changchun University of Science and Technology, 2020Co-Authors: Wang Jing-chuAbstract:University digital campus construction investment in continuously and developing in depth,and the underlying Data cannot be Shared,repeated Data,business processes can't inter-departmental coordination,information isolated island and many other issues,pressing for overall planning of the Data Integration Work of architecture patterns. This article first analyzes the status quo of Data Integration in university,compares several kinds of overall architecture mode of analysis,put forward suitable for the Data Integration Work hub overall architecture model,design a Data flow diagram of hub type architecture,analyzes the Data flow,architecture characteristic advantage,results indicate that hub architecture is suitable for the Data Integration Work of architecture patterns.
Evan Levy - One of the best experts on this subject based on the ideXlab platform.
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customer Data Integration reaching a single version of the truth
2006Co-Authors: Jill Dyche, Evan LevyAbstract:Foreword. Introduction. Acknowledgment. Chapter 1. Executives Flying Blind. Slouching toward Customer Focus. Management Mandates Customer Intimacy. Data Back in the Limelight. What We Don't Know Can Hurt Us. CDI and CRM: A Rapprochement. Manager Do's and Don'ts. Chapter 2. Master Data Management and Customer Data Integration Defined. Delineating the Boundaries of CDI. A CDI Taxonomy. Components of CDI. Manager Do's and Don'ts. Chapter 3. Challenges of Data Integration. Data-Always the Bridesmaid. Five Mainstay Challenges of Data Integration. Manager Do's and Don'ts. Chapter 4. "Our Data Sucks!": The (Not So Little) Secret about Bad Data. Data Quality: The Movie. Bad Data's High Cost. Data Quality: Job Number Two. Data Quality and Master Data Management. Manager Do's and Don'ts. Chapter 5. Customer Data Integration Is Different: A CDI Development FrameWork. Not Your Father's Development Methodology. Top-Down versus Bottom-Up. A CDI Implementation FrameWork. Change Management for CDI. Manager Do's and Don'ts. Chapter 6. Who Owns the Data Anyway?: Data Governance, Data Management, and Data Stewardship. Sturm und Drang of Data Ownership. The Truth about Managing Data as an Asset. A Case for Data Governance. Organizing around Data. Challenges of Adoption and Consensus. Coming Full Circle: Data Management and CDI. Manager Do's and Don'ts. Chapter 7. Making Customer Data Integration Work. Responsibilities of a CDI Architecture. Data Integration the Old-Fashioned Way. Data Integration via CDI. How It Works: Core Functionality of the CDI Hub. Eight Core Functions of Hub Processing. Synchronizing the Hub and Source System. Integrating Multiple Systems with the CDI Hub. Source System Data: Persistent Storage versus Registry Access. The CDI Hub in the IT Architecture. Manager Do's and Don'ts. Chapter 8. Making the Case for Customer Data Integration. Benefits of CDI Investment. Building the Business Case. Keeping the Saboteurs at Bay. Internal Public Relations for CDI. Manager Do's and Don'ts. Chapter 9. Bootstrapping Your Customer Data Integration Initiative. Getting CDI Right. Building the CDI Team. Fierce Conversations: Talking to CDI Vendors. Manager Do's and Don'ts. Glossary. Index.
Anthony David Giordano - One of the best experts on this subject based on the ideXlab platform.
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Data Integration Blueprint and Modeling: Techniques for a Scalable and Sustainable Architecture
2011Co-Authors: Anthony David GiordanoAbstract:Making Data Integration Work: How to Systematically Reduce Cost, Improve Quality, and Enhance EffectivenessTodays 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 books appendices bring together key principles, detailed models, and a complete Data Integration glossary.Coverage includes Implementing repeatable, efficient, and well-documented processes for integrating DataLowering costs and improving quality by eliminating unnecessary or duplicative Data IntegrationsManaging the high levels of complexity associated with integrating business and technical DataUsing intuitive graphical design techniques for more effective process and Data Integration modelingBuilding end-to-end Data Integration applications that bring together many complex Data sources
Jill Dyche - One of the best experts on this subject based on the ideXlab platform.
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customer Data Integration reaching a single version of the truth
2006Co-Authors: Jill Dyche, Evan LevyAbstract:Foreword. Introduction. Acknowledgment. Chapter 1. Executives Flying Blind. Slouching toward Customer Focus. Management Mandates Customer Intimacy. Data Back in the Limelight. What We Don't Know Can Hurt Us. CDI and CRM: A Rapprochement. Manager Do's and Don'ts. Chapter 2. Master Data Management and Customer Data Integration Defined. Delineating the Boundaries of CDI. A CDI Taxonomy. Components of CDI. Manager Do's and Don'ts. Chapter 3. Challenges of Data Integration. Data-Always the Bridesmaid. Five Mainstay Challenges of Data Integration. Manager Do's and Don'ts. Chapter 4. "Our Data Sucks!": The (Not So Little) Secret about Bad Data. Data Quality: The Movie. Bad Data's High Cost. Data Quality: Job Number Two. Data Quality and Master Data Management. Manager Do's and Don'ts. Chapter 5. Customer Data Integration Is Different: A CDI Development FrameWork. Not Your Father's Development Methodology. Top-Down versus Bottom-Up. A CDI Implementation FrameWork. Change Management for CDI. Manager Do's and Don'ts. Chapter 6. Who Owns the Data Anyway?: Data Governance, Data Management, and Data Stewardship. Sturm und Drang of Data Ownership. The Truth about Managing Data as an Asset. A Case for Data Governance. Organizing around Data. Challenges of Adoption and Consensus. Coming Full Circle: Data Management and CDI. Manager Do's and Don'ts. Chapter 7. Making Customer Data Integration Work. Responsibilities of a CDI Architecture. Data Integration the Old-Fashioned Way. Data Integration via CDI. How It Works: Core Functionality of the CDI Hub. Eight Core Functions of Hub Processing. Synchronizing the Hub and Source System. Integrating Multiple Systems with the CDI Hub. Source System Data: Persistent Storage versus Registry Access. The CDI Hub in the IT Architecture. Manager Do's and Don'ts. Chapter 8. Making the Case for Customer Data Integration. Benefits of CDI Investment. Building the Business Case. Keeping the Saboteurs at Bay. Internal Public Relations for CDI. Manager Do's and Don'ts. Chapter 9. Bootstrapping Your Customer Data Integration Initiative. Getting CDI Right. Building the CDI Team. Fierce Conversations: Talking to CDI Vendors. Manager Do's and Don'ts. Glossary. Index.
Iadh Ounis - One of the best experts on this subject based on the ideXlab platform.
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ESWC - Can RDB2RDF Tools Feasibily Expose Large Science Archives for Data Integration
Lecture Notes in Computer Science, 2009Co-Authors: Alasdair J. G. Gray, Norman Gray, Iadh OunisAbstract:Many science archive centres publish very large volumes of image, simulation, and experiment Data. In order to integrate and analyse the available Data, scientists need to be able to (i) identify and locate all the Data relevant to their Work; (ii) understand the multiple heterogeneous Data models in which the Data is published; and (iii) interpret and process the Data they retrieve. rdf has been shown to be a generally successful frameWork within which to perform such Data Integration Work. It can be equally successful in the context of scientific Data, if it is demonstrably practical to expose that Data as rdf . In this paper we investigate the capabilities of rdf to enable the Integration of scientific Data sources. Specifically, we discuss the suitability of sparql for expressing scientific queries, and the performance of several triple stores and rdbrdf tools for executing queries over a moderately sized sample of a large astronomical Data set. We found that more research and improvements are required into sparql and rdbrdf tools to efficiently expose existing science archives for Data Integration.
