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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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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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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business intelligence‚ data warehouse‚ data mining‚ text and web mining‚ and knowledge management. Justify and synthesis your answers/viewpoints with examples (e.g. eBay case) and findings from literature/articles. To understand the relationships between these terms‚ definition of each term should be illustrated. Firstly‚ business intelligence (BI) in most resource has been defined as a broad term that combines many tools and technologies‚ used to extract useful meaning of enterprise data in order to help
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DataBig Data and Future of Data-Driven Innovation A. A. C. Sandaruwan Faculty of Information Technology University of Moratuwa chanakasan@gmail.com The section 2 of this paper discuss about real world examples of big data application areas. The section 3 introduces the conceptual aspects of Big Data. The section 4 discuss about future and innovations through big data. Abstract: The promise of data-driven decision-making is now being recognized broadly‚ and there is growing enthusiasm
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Data Analysis‚ Presentation & Interpretation Prof. Dr. Md. Nazrul Islam Ph.D 1 Data Analysis Plan The appropriate methods of data analysis are determined by your data types and variables of interest‚ the actual distribution of the variables‚ and the number of cases. 2 Data Management 3 Why prepare a plan for processing and analysis of data? All information has been collected in a standardized way Not collected unnecessary data which will never be analyzed A statistical analysis plan should
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CUSTOMER DATA In the term of customer data‚ technology now day give a big role to evaluate the concepts by the overall to moving ownership of the customer when they are away from the individual departments and different it at the enterprise level. In the customer relationship management concept‚ individual that in the each department has responsible for the customer. The success factor for Customer Relationship Management (CRM) is by deploying technology that provides various levels of data access
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Relationship Between Data and Information Data and information are the crucial key elements of information science and information engineering. Using the concepts of information engineering and science helps to develop information systems. Data and information are the common words used in the information and health science world. This paper will help us to understand about data‚ information‚ and their relationship‚ and how effectively they are used in the information systems world. Data are just facts
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CIS 501: Information Systems for Managers Data Mining Problems Introduction Problem 1: Data-Based Decision Making Problem 2: Market Basket Analysis: Association Analysis Problem 3: Market Basket Analysis: Concept Tree/Sequence Analysis Problem 4: Decision Tree Problem 5: Clustering/Nearest Neighbor Classification Problem 6: Clustering Problem 1: Data-Based Decision Making Supermarket Product Placement Suppose that we are responsible for managing product placement within a
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DC10 PART-I DATABASE MANAGEMENT SYSTEMS TYPICAL QUESTIONS & ANSWERS OBJECTIVE TYPE QUESTIONS Each question carries 2 marks. Choose the correct or best alternative in the following: Q.1 In the relational modes‚ cardinality is termed as: (A) Number of tuples. (B) Number of attributes. (C) Number of tables. (D) Number of constraints. Ans: A Q.2 Relational calculus is a (A) Procedural language. (C) Data definition language. Ans: B Q.3 The view of total database content is (A) Conceptual view
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