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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1) ________ is data that has been organized or presented in a meaningful fashion. 1) _______ A) A number B) Information C) A symbol D) A character 2) Which of the following is NOT one of the four major data-processing functions of a computer? 2) _______ A) storing the data or information B) gathering data C) analyzing the data or information D) processing data into information
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Data Mining DeMarcus Montgomery Dr. Janet Durgin CIS 500 June 9‚ 2013 Determine the benefits of data mining to the businesses when employing 1. Predictive analytics to understand the behavior of customers Predictive analytics is business intelligence technology that produces a predictive score for each customer or other organizational element. Assigning these predictive scores is the job of a predictive model‚ which has‚ in turn been trained over your data‚ learning from the experience
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Data Gathering ➢ used to discover business information details to define the information structure ➢ helps to establish the priorities of the information needs ➢ further leads to opportunities to highlight key issues which may cross functional boundaries or may touch on policies or the organization itself ➢ highlighting systems or enhancements that can quickly satisfy cross-functional information needs ➢ a complicated task especially in a large and complex system ➢ must
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Module 815 Data Structures Using C M. Campbell © 1993 Deakin University Module 815 Data Structures Using C Aim After working through this module you should be able to create and use new and complex data types within C programs. Learning objectives After working through this module you should be able to: 1. Manipulate character strings in C programs. 2. Declare and manipulate single and multi-dimensional arrays of the C data types. 3. Create‚ manipulate and manage C pointers
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Big Data In It terminology‚ Big Data is looked as a group of data sets‚ which are so sophisticated and large that the data can not be easily taken‚ stored‚ searched‚ shared‚ analyzed or visualized making use of offered tools. In global market segments‚ such “Big Data” generally looks throughout attempts to identify business tendencies from accessible files sets. Other areas‚ exactly where Big Data continually appears include various job areas of research for example the human being genome and also
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Big data describes innovative methods and technologies to capture‚ distribute‚ manage and analyze larger-sized data sets with high rate and diverse structures that conventional data management methods are unable to handle. Digital data is now everywhere—in every sector public or private‚ economy‚ organization and customer of digital technology. There are many ways that big data can be used to create value across sectors of the global economy. It has demonstrated the capacity to improve predictions
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(UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia R. Badlishah Ahmad University Malaysia Perlis (UniMAP) School of Communication and Computer Engineering Perlis‚ Malaysia Osamah M. Al-Qershi School of Electrical & Electronic Engineering University of Science Malaysia (USM) Penang‚ Malaysia Abstract Steganography is one of the methods used for the hidden exchange of information and it can be defined as the study of invisible communication that usually deals with the
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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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decision is more complex; therefore‚ the processing burden on network nodes increases. (2) In most cases‚ adaptive strategies depend on status information that is collected at one place but used at another. There is a tradeoff here between the quality of the information and the amount of overhead. The more information that is exchanged‚ and the more frequently it is exchanged‚ the better will be the routing decisions that each node makes. On the other hand‚ this information is itself a load
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