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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 Flow

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    DATA FLOW DIAGRAM - one of the most commonly used modeling tool which graphically represents a system as a network of processes‚ linked together through input and output flow lines and entities. Data flow Components ▪ Process - transformation of data flow into outgoing data flow. It may represent . . - whole system - subsystem - activity ▪ Data store - repository of data in the system It may represent . . . - computer file or

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    Data Models

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    Data Models Consider a simple student registration. Specifically we want to support the tasks of students registering for or withdrawing from a class. To do this‚ the system will need to record data about what entities? What specific data about the entities will need to be stored? What is the cardinality between students and courses? Diagram the data model. While‚ considering a student class registration system for registering or withdrawing a system must have the capability to record data in

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    Big Data

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    Big Data which companies are easily able to collect from their businesses‚ customers and employees. It explains the numerous advantages of using the data collected by companies effectively so that it can be used by the company in improving its efficiencies‚ sales‚ faster and quicker turnaround which in turn would lead to increase revenues and finally increased profits (which is what the stakeholders of the company are looking for).It illustrates the prominent fact that companies that are data-driven

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    Data Warehouse

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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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    data abstruction

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    WEEK-3 Data Abstraction Destructors • Destructors are functions without any type • The name of a destructor is the character ’~’ followed by class name – For example: ~clockType(); • A class can have only one destructor – The destructor has no parameters • Destructor automatically executes when the class object goes out of scope C++ Programming: Program Design Including Data Structures‚ Sixth Edition 2 Data Abstract‚ Classes‚ and Abstract Data Types • Abstraction – i

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    Data Mining

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    Data mining Data mining is simply filtering through large amounts of raw data for useful information that gives businesses a competitive edge. This information is made up of meaningful patterns and trends that are already in the data but were previously unseen. The most popular tool used when mining is artificial intelligence (AI). AI technologies try to work the way the human brain works‚ by making intelligent guesses‚ learning by example‚ and using deductive reasoning. Some of the more popular

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    Data Dictionary

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    What Is A Data Dictionary? Per the IBM Dictionary of Computing‚ a data dictionary is a "centralized repository of information about data such as meaning‚ relationships to other data‚ origin‚ usage‚ and format." Essentially‚ it is documentation or data about data in a file or database‚ also called metadata. Typical Items In A Data Dictionary For each field or column in a database table‚ describe the following elements: Item Contents File/Table Name The name of the file or table where

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    Data Integrity

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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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    Data Processing

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    240 - INTRODUCTION TO DATA PROCESSING/MANGEMENT OF INFORMATION TECHNOLOGY Objectives To give a thorough and up-to-date grounding in the realities of commercial applications of Information and Communication Technologies (ICT’s). To Examine the roles of data‚ information and knowledge within modern business organisations‚ and the roles that information and communication technologies (ICT’s) play in supporting people and groups within organisations. To provide students with a thorough understanding

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