By John R. Talburt, Yinle Zhou
ISBN-10: 0128005378
ISBN-13: 9780128005378
Entity info existence Cycle for large facts walks you thru the bits and bobs of dealing with entity details so that you can effectively in attaining grasp facts administration (MDM) within the period of huge info. This ebook explains vast data’s influence on MDM and the severe position of entity info administration procedure (EIMS) in profitable MDM. specialist authors Dr. John R. Talburt and Dr. Yinle Zhou supply an intensive historical past within the rules of handling the entity info existence cycle and supply useful counsel and methods for imposing an EIMS, ideas for exploiting allotted processing to deal with large facts for EIMS, and examples from genuine functions. extra fabric at the thought of EIIM and techniques for assessing and comparing EIMS functionality additionally make this e-book acceptable to be used as a textbook in classes on entity and id administration, information administration, consumer dating administration (CRM), and comparable topics.
- Explains the company price and impression of entity details administration procedure (EIMS) and without delay addresses the matter of EIMS layout and operation, a serious factor corporations face whilst enforcing MDM systems
- Offers useful information that will help you layout and construct an EIM procedure that may effectively deal with enormous data
- Details how you can degree and assessment entity integrity in MDM platforms and explains the foundations and techniques that contain EIM
- Provides an knowing of positive factors and services an EIM process must have that would help in comparing advertisement EIM systems
- Includes bankruptcy evaluate questions, routines, information, and loose downloads of demonstrations that use the OYSTER open resource EIM procedure
- Executable code (Java .jar files), regulate scripts, and artificial enter information illustrate a number of features of CSRUD lifestyles cycle equivalent to id trap, identification replace, and assertions
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Extra info for Entity Information Life Cycle for Big Data: Master Data Management and Information Integration
Sample text
First, they were represented by the over-merged EIS, and then after the correction, they are represented by a new EIS with a different identifier. The false negative error occurs when two or more EIS represent the same realworld entity. Clearly this violates entity identity integrity. The correction for this problem is to merge the EIS representing the same entity. Because each EIS should have only one identifier, only one of the original identifiers can survive after the merger, and the other identifiers must be retired.
UNDERSTANDING THE DATA The capture phase starts with a data quality assessment of each source. The assessment includes profiling the sources with a data quality profiling tool to understand the structure and general condition of the data and also visually inspecting the records in order to find specific conditions that may become obstacles to the ER process. The assessment will • • • Identify which attributes are present in each source. Some attributes may be present in some sources, but not others.
If a cluster created by the ER process overlaps with two or more truth set clusters, then the ER process has produced some level of linking errors. Measuring the degree of error will be discussed in more detail later. BENCHMARKING Benchmarking, on the other hand, is the process of comparing one ER outcome to another ER outcome acting on the same set of references. Even though benchmarking is a relative measure, it is useful because most organizations trying to develop a new MDM system rarely start from scratch.
Entity Information Life Cycle for Big Data: Master Data Management and Information Integration by John R. Talburt, Yinle Zhou
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