Stock Exchange forecasting with Data Mining and Text Mining (Marketing and Sales Analysis) Full names : Fahed Yoseph TITLE : Senior software and Database Consultatnt (Founder of Info Technology System) E-mail: Yoseph@info-technology.net Date of submission: Sep 15th of 2013 CONTENTS PAGE Chapter 1 1. ABSTRACT 2 2. INTRODUCTION 3 2.1 The research problem. 4 2.2 The objectives of the proposal. 4 2.3 The Stock Market movement. 5 2.4 Research question(s). 6 2. Background 3. Problem
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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 different‚ depending on the database management system (DBMS)‚ the various
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Systems with Applications 37 (2010) 5259–5264 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty Seyed Mohammad Seyed Hosseini *‚ Anahita Maleki‚ Mohammad Reza Gholamian Industrial Engineering Department‚ Iran University of Science and Technology‚ Tehran‚ Iran a r t i c l e i n f o a b s t r a c t Data mining (DM)
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DATA COMPRESSION The word data is in general used to mean the information in digital form on which computer programs operate‚ and compression means a process of removing redundancy in the data. By ’compressing data’‚ we actually mean deriving techniques or‚ more specifically‚ designing efficient algorithms to: * represent data in a less redundant fashion * remove the redundancy in data * Implement compression algorithms‚ including both compression and decompression. Data Compression
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Nagham Hamid‚ Abid Yahya‚ R. Badlishah Ahmad & Osamah M. Al-Qershi Image Steganography Techniques: An Overview Nagham Hamid University Malaysia Perils (UniMAP) School of Communication and Computer Engineering Penang‚ Malaysia nagham_fawa@yahoo.com Abid Yahya University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia R. Badlishah Ahmad University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia
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research because they allow the researchers to analyze empirical data needed to interpret the findings and draw conclusions based on the results of the research. According to Portney and Watkins (2009)‚ all studies require a description of subjects and responses that are obtained through measuring central tendency‚ so all studies use descriptive statistics to present an appropriate use of statistical tests and the validity of data interpretation. Although descriptive statistics do not allow general
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1 CIPD unit 4DEP - Version 2 18.03.10 Unit title Developing Yourself as an Effective Human Resources or Learning and Development Practitioner Level 4 1 Credit value 4 Unit code 4DEP Unit review date Sept. 2011 Purpose and aim of unit The CIPD has developed a map of the HR profession (HRPM) that describes the knowledge‚ skills and behaviours required by human resources (HR) and learning and development (L&D) professionals. This unit is designed to enable the learner
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Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Key Best Practices Optimize the Central Plant Quick Start Guide to Increase Data Center Energy How To Start A Problem That You Can Fix Data Center energy efficiency is derived from addressing BOTH your hardware equipment AND your infrastructure. Commit to Improved Design and Operations
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Americans leave long electronic trails of private information wherever they go. But too often‚ that data is compromised. When they shop—whether online or at brick and mortar stores—retailers gain access to their credit card numbers. Medical institutions maintain patient records‚ which are increasingly electronic. Corporations store copious customer lists and employee Social Security numbers. These types of data frequently get loose. Hackers gain entry to improperly protected networks‚ thieves steal employee
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Big Data which companies are easily able to collect from their businesses‚ customers and employees. It explains the numerous advantages of using the data collected by companies effectively so that it can be used by the company in improving its efficiencies‚ sales‚ faster and quicker turnaround which in turn would lead to increase revenues and finally increased profits (which is what the stakeholders of the company are looking for).It illustrates the prominent fact that companies that are data-driven
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