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    Fig: Architecture of data warehouse Operations Conceiving data as a cube with hierarchical dimensions leads to conceptually straightforward operations to facilitate analysis. Aligning the data content with a familiar visualization enhances analyst learning and productivity.[5] The user-initiated process of navigating by calling for page displays interactively‚ through the specification of slices via rotations and drill down/up is sometimes called "slice and dice". Common operations

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    Gene Expression Data

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    Microarray Technology | 7 | | 1.2.1 Measuring mRNA levels | 7 | | 1.2.2 Pre-processing of Gene Expression Data | 8 | | 1.2.3 Applications of Clustering Gene Expression Data | 9 | | 1.3 Mutual Information | 10 | | 1.4 Introduction to Clustering Techniques | 11 | | 1.4.1 Clusters and Clustering | 11 | | 1.4.2 Categories of Gene Expression Data Clustering | 11 | | 1.5 Semi-supervised Learning | 12 | | 1.5.1 Semi-supervised Classification |

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    Qantas Data Book

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    Qantas Data Book 2012 DISCLAIMER The information contained in this investor Data Book is intended to be a general summary of Qantas Airways Limited (Qantas/Qantas Group) and its activities as at 17 October 2012 or otherwise the date specified in the relevant information and does not purport to be complete in any respect. The information in this document is not advice about shares in Qantas (or any other financial product)‚ nor is it intended to influence‚ or be relied upon by‚ any person in

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    Rei Data Warehousing

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    What is a data warehouse and why is REI building one? A data warehouse can be described as a “database that stores current and historical data of potential interest to decision makers throughout a company. The data originate in many core operational transaction systems‚ such as systems for sales‚ customer accounts‚ and manufacturing‚ and may include data from Web site transactions.1” REI is building a data warehouse to improve the company and to meet the needs of the customers. REI’s data warehouse

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    Data and decision making

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    Tasmania BEA 654 Data and Business Decision Making Semester 2‚ 2013 CRICOS Provider Code 00586B Partial Solutions to Problem Set 2—Week 3 1. Arithmetic mean= (-0.5+1)/2=0.25 (25%) Geometric mean of annual rate of return RG (1  RG ) 2  (1  (0.5))(1  1)  R G  0 .5 * 2  1  0 The geometric mean reflects the true return of the investment. 2. c. See the formula. 3. The whole crew is present‚ so this is a population. X = 567‚ X2 = 48‚165‚ N=7 Ordered array of data: 49‚ 63‚ 77‚ 85

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    Data Collection Paper

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    Data Collection Paper Week 2 Research Paper RES 341 University of Phoenix Real Estate Research Week 2 Real Estate provides individuals with a source of investment for his or her future. Owning a piece of real estate could be a business investment‚ or in the case of this research‚ a home for an individual or a family. When a person purchases a home there are many things to consider. The most common information to review is square footage‚ price‚ amount of bedrooms‚ bathrooms

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    Hindi Tv Data

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    Market The market size of Television industry is around 20‚000 crores; out of which: 8‚000 crores is Advertisement market. 12‚000 crores is encrypted/pay channels. *Source: Price Waterhouse Cooper (PWC) report-2008. * 3. Television Viewership Data * The total viewership in Indian TV market is 112 million; out of which: Cable & Satellite viewership= 78 million Terrestrial/Regional= 30 million Direct To Home (DTH)= 4 million IPTV ( Internet Protocol Television) & Mobile platforms have

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    Data Flow Diagram

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    Figure 3.0: First Entry Level Data Flow Diagram of Administrator’s Capabilities The figure 3.0 represents the First Entry Level Data Flow Diagram of the Administrator upon successfully logging-in into the Generic Academic Performance Monitoring System. He/she has the capabilities to manage modules such as handling member’s account‚ importing students list‚ creation of grade criteria‚ grade computation and updating website contents Figure 3.1: Second Entry Level Data Flow Diagram on Managing Account

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    Big Data In Sports

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    argued that Big Data can impact the society in various ways. Studies (e.g. Ranadive‚ 2014; Schonberger & Cukier‚ 2013; Bustamante et al.‚ 2013) have suggests that big data can greatly and positively impact the society. It can positively impact the educational and medical fields‚ solve seemingly intractable problems that people experience‚ and impact on the professional sports. Big data are social construction and neutral elements of reality. Butte et al. (2013) argues that Big data can be used an important

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    Data Base Queries

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    Data base • What are Group Functions? • Types of Group Functions • Using Group Functions • Using AVG and SUM function • Using MIN and MAX function • Using COUNT function • Group functions and NULL values • Using the NVL Function with Group Function • Creating GROUP of DATA • Creating GROUP of DATA:GROUP BY Clause • Using the GROUP BY clause • Grouping by more than one column • Using the GROUP BY clause on multiple columns • Illegal

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