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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An Oracle White Paper July 2010 Data Masking Best Practices Oracle White Paper—Data Masking Best Practices Executive Overview ........................................................................... 1 Introduction ....................................................................................... 1 The Challenges of Masking Data ....................................................... 2 Implementing Data Masking .............................................................. 2
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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 Mining Melody McIntosh Dr. Janet Durgin Information Systems for Decision Making December 8‚ 2013 Introduction Data mining‚ or knowledge discovery‚ is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends‚ allowing businesses to make proactive‚ knowledge- driven decisions Although data mining is still in its infancy
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Ensuring Data Storage Security in Cloud Computing Cong Wang‚ Qian Wang‚ and Kui Ren Department of ECE Illinois Institute of Technology Email: {cwang‚ qwang‚ kren}@ece.iit.edu Wenjing Lou Department of ECE Worcester Polytechnic Institute Email: wjlou@ece.wpi.edu Abstract—Cloud Computing has been envisioned as the nextgeneration architecture of IT Enterprise. In contrast to traditional solutions‚ where the IT services are under proper physical‚ logical and personnel controls‚ Cloud Computing
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Data Services Vodafone’s Data Services are tailored to make you stay competitive even as your needs change. We provide simplified network solutions to improve your productivity and also offer customized solutions that save organizations from having to deal with multiple providers. We offer entry-level products using ADSL technology to high-end solutions delivered through a mix of ATM‚ Frame Relay or IP/VPN over MPLS-established technologies that alleviate pressure on your IT resources and give you
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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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Introduction 1.1 The Relational Data Model Revisited 1.2 The Vocabulary of Security and Major DB Security Threats 2. Database Security Models 2.1 Discretionary Security Models 2.2 Mandatory Security Models 2.3 Adapted Mandatory Access Control Model 2.4 Personal Knowledge Approach 2.5 Clark and Wilson Model 2.6 A Final Note on Database Security Models 3. Multilevel Secure Prototypes and Systems 3.1 SeaView 3.2 Lock Data Views 3.3 ASD_Views 4. Conceptual Data Model for Multilevel Security
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Data Warehouse Concepts and Design Contents Data Warehouse Concepts and Design 1 Abstract 2 Abbreviations 2 Keywords 3 Introduction 3 Jarir Bookstore – Applying the Kimball Method 3 Summary from the available literature and Follow a Proven Methodology: Lifecycle Steps and Tracks 4 Issues and Process involved in Implementation of DW/BI system 5 Data Model Design 6 Star Schema Model 7 Fact Table 10 Dimension Table: 11 Design Feature: 12 Identifying the fields from facts/dimensions: MS: 12 Advanced
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Database Models and Legacy Systems The evolution of the information age demanded robust management systems for storing large volume of data‚ efficiency in retrieval‚ and enhanced data security and sharing; hence‚ the development of databases from flat file systems. Despite this advancement in technology‚ many organizations such as Colorado Financial Reporting System (COFRS) continue to use legacy database systems. Colorado Financial Reporting System (COFRS) The Colorado Financial Reporting
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