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    Data Masking

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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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    Data Mining

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    Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology‚ if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining

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    Data Management

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    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. (C) External view. Ans: A Q.4 Cartesian product in relational algebra is (A) a Unary operator. (B) a Binary operator. (C) a Ternary operator. (D) not defined

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    Data Base

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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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    design

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    Health care code requirements Introduction: The health maintenance and enhancement‚ the safety and the comfort of people in health care facilities are seriously affected if specific building requirements are not met. Hospitals are the most complex of building types. Each hospital is comprised of a wide range of services and functional units. Functional organization criteria classify health care facility environments as follows: nursing units‚ operating theatres‚ diagnostic facilities (radiology

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    data mining

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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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    Design

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    prehistoric times. The ancient Egyptians‚ Babylonians‚ and Greeks all used it. The purpose of the arch in these cultures‚ however‚ was limited to supporting small structures‚ such as storerooms‚ and people often used columns to support the roof. This design limited the size and scope of a building. As a result‚ builders could not construct extremely large palaces or government buildings. The ancient Romans created an arch that could support huge amounts of weight. How did the Romans accomplish this

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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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    Sample

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    1.1 Pendahuluan Penyelidikan selalu dikaitkan dengan data numerika. Melalui penyelidikan kuantitatif‚ data numerika yang dikumpul dianalisis dengan menggunakan ujian statistik. Variabel-variabel dalam sesuatu fenomena di bawah kajian dioperasikan dan masalah kajian dinyatakan dalam bentuk hipotesis. Oleh itu sebelum penyelidik memulakan kajian merea perlulah mematuhi beberapa peringkat prosedur menjalan penyelidikan. Semua ini dapaat diringkaskan sebagaiman di bawah: i. Mengenal pasti

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    sample

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    Lewin’s model (as cited in Hall‚ 1997) is ideal for implementing change at the XYZ Plant. This change model has been shown to be effective because it (a) can be applied to any setting‚ (b) is easy to follow‚ and (c) incorporates strategies to identify and resolve obstacles during the change process (Moore‚ 1997). The model will be used to describe the trial implementation of the honor system method for employees in the Engineering Department to document their time worked. The simplicity of Lewin’s

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