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    Data Base

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    Assignment 1: Compare the Different Relational Database Management Systems In an article written By Suqing Wang‚ eHow Contributor SQL Server Vs. Oracle Data Types   Database While designing and defining tables in databases‚ it is important to find out the data type for each column in the data tables. A data type is an attribute which defines the type of data an object can retain: integer‚ string‚ data and time‚ etc. There are basically three main types: text‚ numbers and date/times. The data types are

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    Data Replication

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    Outline  Introduction  Distributed DBMS Architecture  Distributed Database Design  Distributed Query Processing  Distributed Transaction Management  Data Replication  Consistency criteria  Update propagation protocols  Parallel Database Systems  Data Integration Systems  Web Search/Querying  Peer-to-Peer Data Management  Data Stream Management Distributed & Parallel DBMS M. Tamer Özsu Page 6.1 Acknowledgements  Many of these slides are from notes prepared by  Prof. Gustavo

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    Data Warehousing

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    Case Study 2 1.) Compare and contrast Inmon and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach

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    Data Integration

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    Programme Management Office Project Charter & Scope Statement Project Title: Project ID: Project Sponsor: Project Manager: Charter approval date: Project and Module Data Project Brian Norton‚ President Liam Duffy‚ IS Services Document Control Date 30-01-12 02-02-12 10-02-12 16-03-12 Version V 1.0 V 2.0 V 3.0 V 4.0 Changed by Liam Duffy Liam Duffy Liam Duffy Liam Duffy Reasons for Change Original Document Consultation with Sponsor Consultation with Project

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    Data Analysis

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    Yogesh Uppal You are allowed to use a standard size (8.5*11) cheat sheet and a simple calculator. Please write all the answers with a BALL-POINT PEN or an INK PEN. If you have any questions during the exam‚ please raise your hand. GOOD LUCK!!! I am sure you guys will do great. Multiple Choice Identify the letter of the choice that best completes the statement or answers the question and write it in the space given next to the question number. Each multiple choice question is worth 1 point. ____

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    data protection

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    ict policy Data Protection ICT/DPP/2010/10/01   1. Policy Statement 1.1. Epping Forest District Council is fully committed to compliance with the requirements of the Data Protection Act 1998 which came into force on the 1st March 2000. 1.2. The council will therefore follow procedures that aim to ensure that all employees‚ elected members‚ contractors‚ agents‚ consultants‚ partners or other servants of the council who have access to any personal data held by or on behalf of the

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    DATA CLUSTERING

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    WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining

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    Data Sonification

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    Turnage‚ Bonebright‚ Buhman‚ Flowers (1996) showed that untrained participants can listen to shapes. That is‚ they used data sonification – musical representation of two dimensional space‚ with pitch as the vertical dimension and time as the horizontal dimension – to present participants the visual and auditory representation of waveforms. In two conditions‚ they showed the participants could match one visual presentation to one of two auditory representations‚ or match one auditory presentation

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    Data Commentary

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    1097300104 E5B Data Analysis First Part Personal information: including the participants’ gender‚ age‚ educational background‚ marital status and monthly income. Gender As Figure 1 showed‚ there were 45% of female participants and 55% of male. The numbers of the participants of each gender were very close. Age The respondents were all my friends on Facebook; as the result‚ the majority (73%) of their age was in the range of 16-20‚ as seen in Figure 2. Figure 1: Gender of participants

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    Statistics and Data

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    Residuals Date: _____________________ Introduction The fit of a linear function to a set of data can be assessed by analyzing__________________. A residual is the vertical distance between an observed data value and an estimated data value on a line of best fit. Representing residuals on a___________________________ provides a visual representation of the residuals for a set of data. A residual plot contains the points: (x‚ residual for x). A random residual plot‚ with both

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