any browser and on Windows platform. CHAPTER 2 SYSTEM ANALYSIS 2.1 INTRODUCTION Systems analysis is a process of collecting factual data‚ understand the processes involved‚ identifying problems and recommending feasible suggestions for improving the system functioning. This involves studying the business processes‚ gathering operational data‚ understand the information flow‚ finding out bottlenecks and evolving solutions for overcoming the weaknesses of the system so as to achieve the
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Introduction: Recently‚ research in Human Resource (HR) activities that are embedded with Data Mining Techniques can solve unstructured and indistinct decision making problems. Human Resource Management (HRM) activities can facilitate to take fair and consistent decisions‚ and to improve the effectiveness of decision-making processes. Besides the challenges for HR Professionals to manage the organizational decisions and talents‚ especially they have to ensure that the selection of a right
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Big Data‚ Data Mining and Business Intelligence Techniques 2 What is Data? • Data is information in a form suitable for use with a computer. • There are two types of data ▫ Structured ▫ Unstructured • The total volume of data is growing 59% every year. • The number of files grow at 88% every year. 3 What is Big Data? Exa Analytics on Big Data at Rest Up to 10‚000 Times larger Peta Data Scale Giga Data at Rest Tera Data Scale Mega Traditional Data Warehouse
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Handling Consumer Data Introduction When I visit my local Caltex Woolworths petrol station on “cheap fuel Wednesday” to cash in the 8c per litre credit that my Wife earned the previous Friday buying the groceries with our “Everyday Rewards” card‚ I did not‚ until researching this report‚ have any clue as to the contribution I was making to a database of frightening proportions and possibilities… nor that‚ when I also “decide” to pick up the on-sale‚ strategically-placed 600mL choc-milk‚ I am
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Networks Volvo utilized data mining in an effort to discover the unknown valuable relationships in the data collected and to assist in making early predictive information. It created a network of sensors and CPUs that were embedded throughout the cars and from which data was captured. Data was also captured from customer relationship systems (CRM)‚ dealership systems‚ product development and design systems and from the production floors in their factories. The terabytes of data collected was streamed
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Abstract Data mining is one of the most important tools for analyzing information from large databases. The retail industry has recently seen the growing number of data mining applications in reducing time and cost for the industry. The paper defines data mining and the seven operations of data mining that have been classified through many different literatures. It then focus on the important applications of data mining in retail industry including marketing‚ customer relationship management‚ risk
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Department of Education Office of Federal Student Aid Data Migration Roadmap: A Best Practice Summary Version 1.0 Final Draft April 2007 Data Migration Roadmap Table of Contents Table of Contents Executive Summary ................................................................................................................ 1 1.0 Introduction ......................................................................................................................... 3 1.1 1.2 1.3 1.4 Background
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A data mining approach to analysis and prediction of movie ratings M. Saraee‚ S. White & J. Eccleston University of Salford‚ England Abstract This paper details our analysis of the Internet Movie Database (IMDb)‚ a free‚ user-maintained‚ online resource of production details for over 390‚000 movies‚ television series and video games‚ which contains information such as title‚ genre‚ box-office taking‚ cast credits and user ’s ratings. We gather a series of interesting facts and relationships
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Multiple Linear Regression Data Mining for Business Intelligence Shmueli‚ Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors (variable selection) Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target Familiar use of regression in data analysis Multiple linear
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TYPES OF DATA AND COMPONENTS OF DATA STRUCTURES Data types 1. Primitive: is a data type provided by a programming language as a basic building block 2. Composite: is any data type which can be constructed in a program using its programming language’s primitive data types and other composite types 3. Abstract: is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics
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