Started 3 4. ‘Bang for the Buck’ Data Models 23 5. Design Patterns 23 6. Master Data Management (MDM) 36 7. Build your Own 57 8. Generic Data Models 79 9. From the Cradle to the Grave 88 10. Commercial Web Sites 108 11. Vertical Applications 109 Appendix A. Business Rules 114 Appendix B. Glossary of Terms 114 1. Introduction 1.1 Our Approach This book adopts a unique approach which is based on using existing Data Models as the basis for designing
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Secondary research data in a digital age Advantages of secondary data. 1. Availability. Is faster and less expensive when researchers use electronic retrieval to access data stored digitally. 2. Money and time are some things that researchers need to save up and when they can access to the information in such a short amount of time and for free‚ they can be using that extra time and money in some other things 3. Secondary data are essential in instances when data simply cannot be obtained
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X will win is 60% and above.” Null Hypothesis “If X makes the first move then the probability of the player with X will win is less than 60%.” Data Collection and Preparation To prove or refute the hypothesis‚ data has to be collected. As we all know this step requires a great amount of time and effort. Also in order to build an effective model a data mining algorithm must be presented with a few hundred or few thousands relevant/applicable records. As mentioned above there are thousands of winning
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Understand How to Organise and Report Data That Has Been Researched. 1.1 Describe different ways of organising data that has been researched. Data is a group of information that are used for various purposes like analysis‚ evaluations and to arrive at certain results or conclusions. Data reporting is a process where data is extracted form a source or many sources and then converted into a format that can be used for a purpose. It is usually presented in the form of a report in tables‚ charts
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Transforming Logical Data Models into Physical Data Models Susan Dash Ralph Reilly IT610-1404A-01 According to an article written by Tom Haughey the process for transforming a logical data model into a physical data model is: The business authorization to proceed is received. Business requirements are gathered and represented in a logical data model which will completely represent the business data requirements and will be non-redundant. The logical model is then transformed into a first cut physical
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Panasonic Creates a Single Version of the Truth from Its Data important mis case study CASE STUDY 1. Evaluate Panasonic’s business strategy using the competitive forces and value chain models. Panasonic is one of the world’s leading electronics manufacturers. To be effective‚ their goals‚ objectives‚ culture‚ and activities needed to be consistent with their strategy. In order to increase their profit margin‚ they had to find ways to reduce costs and increase sales. For Panasonic‚ this meant
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Terminal Data Capability [What is it?] - Generally‚ It is a data element that stores information particularly on terminal that can be individually addressed by tag ID. Data element resident in the terminal shall be under control of one of the following parties: • Terminal manufacturer: For example‚ IFD Serial Number • Acquirer/Agent: For example‚ Merchant Category Code • Merchant: For example‚ Local Date and Local Time (these may be controlled by either merchant or acquirer)
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Data Marts Advantages The implementation of data marts enable users to gain faster access to common data utilizing a technique called dimensional data modeling‚ which optimizes data for reports. For example‚ since data is prepared in common format‚ users with little or not training at all‚ can browse a data mart and obtain information as needed. Data marts can improve end user response time‚ as it contains raw data which allows computer systems to focus on a single task‚ thus‚ improving performance
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Big Data Architecture‚ Goals and Challenges Cipson Jose Chiriyankandath Dakota State University Abstract Big Data inspired data analysis is matured from proof of concept projects to an influential tool for decision makers to make informed decisions. More and more organizations are utilizing their internally and externally available data with more complex analysis techniques to derive meaningful insights. This paper addresses some of the architectural goals and challenges for Big Data architecture
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SOLUTIONS MANUAL DATA AND COMPUTER COMMUNICATIONS EIGHTH EDITION CHAPTERS 1 - 12 WILLIAM STALLINGS Copyright 2007: William Stallings © 2007 by William Stallings All rights reserved. No part of this document may be reproduced‚ in any form or by any means‚ or posted on the Internet‚ without permission in writing from the author. Selected solutions may be shared with students‚ provided that they are not available‚ unsecured‚ on the Web. -2- NOTICE This manual contains
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