.......................................................................................... 3 2.1.2 Non-functional requirement ............................................................................................. 5 3. Logical design: Data Modeling (ERD) .................................................................................... 6 4. Logical design: Process Modeling (DFD) ............................................................................... 9 5. Decision
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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 unique to data warehousing. Rather than building
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Lecture Notes 1 Data Modeling ADBMS Lecture Notes 1: Prepared by Engr. Cherryl D. Cordova‚ MSIT 1 • Database: A collection of related data. • Data: Known facts that can be recorded and have an implicit meaning. – An integrated collection of more-or-less permanent data. • Mini-world: Some part of the real world about which data is stored in a database. For example‚ student grades and transcripts at a university. • Database Management System (DBMS): A software package/ system to facilitate
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Data Structures and Algorithms DSA Annotated Reference with Examples Granville Barne Luca Del Tongo Data Structures and Algorithms: Annotated Reference with Examples First Edition Copyright c Granville Barnett‚ and Luca Del Tongo 2008. This book is made exclusively available from DotNetSlackers (http://dotnetslackers.com/) the place for .NET articles‚ and news from some of the leading minds in the software industry. Contents 1 Introduction 1.1 What this book is‚ and what
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P Decreased Cardiac Output R/T E Atrial Fibrillation and Mechanical Ventilation AEB S – Client on mechanical ventilation. Albumin 1.1 – 2/4/14 – low osmolality in blood – third spacing. Atrial Fibrilation Sluggish Pupil response Blood pressure 97/39 Heart Rate 54 Peripheral pulses diminished PLANNING ____________________________________________________________________________________ Client Goal: Client will not have decreased Cardiac Output Outcome Criteria
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Hypothesis 7. Chapter Scheme 8. References Retail Store Design: Creating a Powerful Store Image 1. Introduction We ’ve heard it again and again - "You never get a second chance to make a first impression". Even though there are many influences at work in the shopping experience‚ the look of a store holds the most sway in enticing us through the doors. We even tend to sum up that initial in-store encounter in visual terms: a store is exciting‚ clean or well-organized or‚ at the other
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Chapter 1: The Virtual Environment Work Team Introduction A national accounting firm offers tax preparation services to individuals. T.A. Stearns was a popular national accounting firm because of its superior reputation for high quality of its advice and the excellence of its service. Superior computer databases and analysis tools that were highly technical in tax laws they covered and in code they were written was the key to T.A. Stearns reputation. They were perfected by high levels
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Pizza Store Layout Simulation University of Phoenix Introduction The concept of the learning curve is a powerful tool and is applicable to all learning processes. In this simulation I became the manager and ran the Pizza store hoping to produce a better process for the amount of time a customer waits for their order. The goal of my job was to apply the learning curve concepts to test the alternative against the current process of the Pizza store. I will explain and provide information
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Patrick Cunningham ITM220-J November 8‚ 2013 Big Data Big Data‚ an inspirational novel about the collection and processing of massive amounts of data was eye-opening and encouraging. This collection of data over a long period of time has been processed and used towards many different aspects throughout the world. Dilemmas such as tracking the H1N1 virus‚ to buying the most inexpensive plane tickets‚ all the way to predicting dangerous manholes explosions have all been processed and tabulated
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5.3.3 Data cleaning Data cleaning helps to remove all unnecessary data. Data cleaning attempts to fill in missing values‚ smooth out noise while identifying outliers and correct inconsistencies in the data. Data cleaning is usually an iterative two-step process consisting of discrepancy detection and data transformation. 5.3.4 Data analysis Data analysis is also known as analysis of data or data analytics‚ is a process of inspecting‚ cleansing‚ transforming and modeling data with the goal of discovering
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