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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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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BCSCCS 303 R03 DATA STRUCTURES (Common for CSE‚ IT and ICT) L T P CREDITS 3 1 0 4 UNIT - I (15 Periods) Pseudo code & Recursion: Introduction – Pseudo code – ADT – ADT model‚ implementations; Recursion – Designing recursive algorithms – Examples – GCD‚ factorial‚ fibonnaci‚ Prefix to Postfix conversion‚ Tower of Hanoi; General linear lists – operations‚ implementation‚ algorithms UNIT - II (15 Periods)
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Primary sector of the economy The primary sector of the economy is the sector of an economy making direct use of natural resources. This includes agriculture‚ forestry and fishing‚ mining‚ and extraction of oil and gas. This is contrasted with the secondary sector‚ producing manufactures and other processed goods‚ and the tertiary sector‚ producing services. The primary sector is usually most important in less developed countries‚ and typically less important in industrial countries. The manufacturing
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Secondary Applications Listed below are a number of example secondary application prompts. The purpose of this section is to get you familiar with the types of questions secondaries ask. This publication makes no claim regarding the accuracy of this section. The question prompts contained herein may not be up to date or even correct in any way. The questions that follow were not reviewed or approved by the respective medical schools; rather‚ the list was generated by students. Under no circumstances
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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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WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining
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Introduction to Data Modeling and MSAccess CONTENT 1 2 3 4 5 6 Introduction to Data Modeling ............................................................................................................... 5 1.1 Data Modeling Overview ............................................................................................................... 5 1.1.1 Methodology .......................................................................................................................... 6 1.1.2 Data Modeling
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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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Types of Data Integrity This section describes the rules that can be applied to table columns to enforce different types of data integrity. Null Rule A null rule is a rule defined on a single column that allows or disallows inserts or updates of rows containing a null (the absence of a value) in that column. Unique Column Values A unique value rule defined on a column (or set of columns) allows the insert or update of a row only if it contains a unique value in that column (or set of columns)
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