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    Data Warehousing

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    Data Warehousing‚ Data Marts and Data Mining Data Marts A data mart is a subset of an organizational data store‚ usually oriented to a specific purpose or major data subject‚ that may be distributed to support business needs. Data marts are analytical data stores designed to focus on specific business functions for a specific community within an organization. Data marts are often derived from subsets of data in a data warehouse‚ though in the bottom-up data warehouse design methodology the data

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    Big Data

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    .......................................................................................... 3 2.1.2 Non-functional requirement ............................................................................................. 5 3. Logical design: Data Modeling (ERD) .................................................................................... 6 4. Logical design: Process Modeling (DFD) ............................................................................... 9 5. Decision

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    Data Security

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    Subnetting __________. b. is also called subnet addressing 2. A virtual LAN (VLAN) allows devices to be grouped __________. a. logically 3. Convergence combines voice‚ data‚ and video traffic __________. a. over a single IP network 4. Each of the following is a convergence security vulnerability except __________. a. convergence resource attacks (CRA) 5. Which of the following is not true regarding a demilitarized zone (DMZ)? a. It contains servers that are only used by

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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 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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    Wastewater Data

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    Process in Houston CIVT 301 Outline Wastewater collection data in Houston What is sewage treatment? Where does wastewater come from? Factors that affect the flow of pipelines Industrial wastewater? Storm water/ Data The treatment plant operator Sources of wastewater Why treat wastes Waste water treatment facilities Treatment processes Drinking water What can be done to help? Wastewater collection data in Houston 640 square miles area 3 million citizens served 6‚250 miles

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    Data Preprocessing

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    professional services business processes. I’m not pumping products here-I’m pointing to business processes that need to evolve to improve profitability. Here are five ways social CRM can help. 1. You Can’t Make Support Access Too Easy. Making it easier for customers to find the specific support resource they need is half the battle of making support feel easy to use. Customers are easily overwhelmed by large companies’ websites‚ online resources and call center processes. (Ironically‚ these were the

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    Big Data

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    having too much data‚ and what to do about them There is rarely an instance of business that you can encounter that does not involve the processing of data on information systems these days. Businesses and organizations use information systems in a majority of their functions‚ and as a result‚ are creating mass amounts of data. Because data is so crucial to business operations‚ it is being gathered‚ stored‚ and utilized in exponential amounts compared to the previous decade. These data stores can

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    Data Quality

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    PRINCIPLES OF DATA QUALITY Arthur D. Chapman1 Although most data gathering disciples treat error as an embarrassing issue to be expunged‚ the error inherent in [spatial] data deserves closer attention and public understanding …because error provides a critical component in judging fitness for use. (Chrisman 1991). Australian Biodiversity Information Services PO Box 7491‚ Toowoomba South‚ Qld‚ Australia email: papers.digit@gbif.org 1 © 2005‚ Global Biodiversity Information Facility Material

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    coherent digital envronment‚ including large main frames‚ desktop and laptop computers‚ pda’s‚ and internet appliances. Telecommunications services that provide data ‚ voice and video connectivity to employee‚ customers‚ and suppliers. Data management services that store and manage corporate data and provide acapabilities for analyzing the data. Applications software service that provide enterprise wide capabilities such as enterprise resource planning‚ customer relationsip management‚ supply chain

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