The Experts below are selected from a list of 912 Experts worldwide ranked by ideXlab platform
Yinle Zhou - One of the best experts on this subject based on the ideXlab platform.
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Entity IdEntity Information and the CSRUD Life Cycle Model
Entity Information Life Cycle for Big Data, 2020Co-Authors: John R. Talburt, Yinle ZhouAbstract:Chapter 2 lays the foundation for the book’s theme – recognizing and understanding the role of life cycle management in the context of Entity Information supporting master data management. The chapter defines a life cycle model called CSRUD as an extension and adaptation of existing models for general Information life cycle management to the specific context of Entity idEntity Information.
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Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
2015Co-Authors: John R. Talburt, Yinle ZhouAbstract:Entity Information Life Cycle for Big Data walks you through the ins and outs of managing Entity Information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big datas impact on MDM and the critical role of Entity Information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the Entity Information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on Entity and idEntity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of Entity Information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate Entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as idEntity capture, idEntity update, and assertions
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Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
Entity Information Life Cycle for Big Data: Master Data Management and Information Integration, 2015Co-Authors: John R. Talburt, Yinle Zhou, Yan ZhouAbstract:Entity Information Life Cycle for Big Data walks you through the ins and outs of managing Entity Information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of Entity Information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the Entity Information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on Entity and idEntity management, data management, customer relationship management (CRM), and related topics.
John R. Talburt - One of the best experts on this subject based on the ideXlab platform.
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Entity IdEntity Information and the CSRUD Life Cycle Model
Entity Information Life Cycle for Big Data, 2020Co-Authors: John R. Talburt, Yinle ZhouAbstract:Chapter 2 lays the foundation for the book’s theme – recognizing and understanding the role of life cycle management in the context of Entity Information supporting master data management. The chapter defines a life cycle model called CSRUD as an extension and adaptation of existing models for general Information life cycle management to the specific context of Entity idEntity Information.
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Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
2015Co-Authors: John R. Talburt, Yinle ZhouAbstract:Entity Information Life Cycle for Big Data walks you through the ins and outs of managing Entity Information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big datas impact on MDM and the critical role of Entity Information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the Entity Information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on Entity and idEntity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of Entity Information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate Entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as idEntity capture, idEntity update, and assertions
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Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
Entity Information Life Cycle for Big Data: Master Data Management and Information Integration, 2015Co-Authors: John R. Talburt, Yinle Zhou, Yan ZhouAbstract:Entity Information Life Cycle for Big Data walks you through the ins and outs of managing Entity Information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of Entity Information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the Entity Information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on Entity and idEntity management, data management, customer relationship management (CRM), and related topics.
Yasushi Umeda - One of the best experts on this subject based on the ideXlab platform.
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Simulating Life Cycles of Individual Products for Life Cycle Design
Procedia CIRP, 2020Co-Authors: Yuki Matsuyama, Shinichi Fukushige, Yasushi UmedaAbstract:Abstract Minimizing environmental loads and resource consumption is a major issue in the manufacturing industry while enhancing value of products. Individual products have a variety of states depending on their life cycle histories even if they are produced from the same design Information. While design Information represents their ‘nominal Information’ specified by designers, we call a specific state of an individual product as ‘Entity Information.’ The differences in individual products make it instable in terms of the states and quantity to circulate resources and to deliver service activities at the after sales market. To realize efficient resource circulation and high quality services, designers should determine nominal Information of a product and of its life cycle flow by analyzing the Entity Information at its design stage. This paper proposes a method for modeling both nominal Information of a product life cycle and the Entity Information. This method represents the nominal Information with product model and life cycle flow model. In this paper, we define a model of the Entity Information, which shows states of individual products and the number of the products in each life cycle process such as maintenance, collection, and end-of-life treatments. To create this model, we derive Entity Information throughout the entire life cycle flow by using life cycle simulation technique. A case study of a smart phone is illustrated for demonstrating the feasibility of the proposed modeling method.
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Study of Life Cycle Design Focusing on Resource Balance throughout Product Life Cycles
Procedia CIRP, 2020Co-Authors: Yuki Matsuyama, Shinichi Fukushige, Tomohiko Matsuno, Yasushi UmedaAbstract:Abstract Life cycle design is a promising approach for introducing efficient resource circulation. In such design, there are difficulties in balancing demand and supply for resources throughout product life cycles. For the resource balance, it is important to design a product life cycle focusing on individual products and their parts, since they change their states diversely and flow along different circulation paths through their life cycles. This paper proposes a modelling method for the individual products and parts in addition to its design Information. The design Information is the nominal Information of the product specified by designers. To achieve this, this paper defines three models; hierarchical structure model, life cycle flow model, and Entity Information model. Hierarchical structure model represents design Information of a product. Life cycle flow model represents a network of processes included in product life cycles. Entity Information model represents Information of individual products and parts. The Information indicates when each product and part flows along which circulation paths in which state. With this Information, this method represents resource balance throughout product life cycles. Moreover, this study employs life cycle simulation technique to derive the Entity Information model from the hierarchical structure model and life cycle flow model. We demonstrate the proposed modelling method via its application to a smart phone in a case study.
Yan Zhou - One of the best experts on this subject based on the ideXlab platform.
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Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
Entity Information Life Cycle for Big Data: Master Data Management and Information Integration, 2015Co-Authors: John R. Talburt, Yinle Zhou, Yan ZhouAbstract:Entity Information Life Cycle for Big Data walks you through the ins and outs of managing Entity Information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of Entity Information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the Entity Information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on Entity and idEntity management, data management, customer relationship management (CRM), and related topics.
Ximiao Yu - One of the best experts on this subject based on the ideXlab platform.
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edcleaner data cleaning for Entity Information in social network
International Conference on Communications, 2019Co-Authors: Jinlin Wang, Hongli Zhang, Binxing Fang, Xing Wang, Ximiao YuAbstract:The application of social network has produced a large amount of Entity data in different formats, which accompanied by problems such as data offset and attribute missing. The existing research on dealing with multiple data scenarios and accuracy performance is insufficient. To solve the problem of data cleaning, EDCleaner is carried out on transforming Entity Information into structured data with attribute labeling. The method of attribute recognition and data normalization for semi-structured data is proposed in EDCleaner, which efficiently identifies the attribute tag relationship of data and obtains the structured data with uniform specifications. Furthermore, a data cleaning model with active learning extension is established. The machine learning classifier is used to further improve the accuracy of attribute recognition, and finally form an efficient and accurate data cleaning method. Experimental results show that EDCleaner improves the cleaning accuracy and other performing indicators of Entity Information and exceeds the level of state of the art.
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ICC - EDCleaner: Data Cleaning for Entity Information in Social Network
ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019Co-Authors: Jinlin Wang, Hongli Zhang, Binxing Fang, Xing Wang, Ximiao YuAbstract:The application of social network has produced a large amount of Entity data in different formats, which accompanied by problems such as data offset and attribute missing. The existing research on dealing with multiple data scenarios and accuracy performance is insufficient. To solve the problem of data cleaning, EDCleaner is carried out on transforming Entity Information into structured data with attribute labeling. The method of attribute recognition and data normalization for semi-structured data is proposed in EDCleaner, which efficiently identifies the attribute tag relationship of data and obtains the structured data with uniform specifications. Furthermore, a data cleaning model with active learning extension is established. The machine learning classifier is used to further improve the accuracy of attribute recognition, and finally form an efficient and accurate data cleaning method. Experimental results show that EDCleaner improves the cleaning accuracy and other performing indicators of Entity Information and exceeds the level of state of the art.