Transforming Logical Data Models into Physical Data Models Susan Dash Ralph Reilly IT610-1404A-01 According to an article written by Tom Haughey the process for transforming a logical data model into a physical data model is: The business authorization to proceed is received. Business requirements are gathered and represented in a logical data model which will completely represent the business data requirements and will be non-redundant. The logical model is then transformed into a first cut physical
Premium Data modeling SQL Database
Data mining and warehousing and its importance in the organization Data Mining Data mining is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue‚ cuts costs‚ or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles‚ categorize it‚ and summarize the relationships identified. Technically‚ data
Premium Data mining
Financial Services Data Management: Big Data Technology in Financial Services Big Data Technology in Financial Services Introduction: Big Data in Financial Services ....................................... 1 What is Driving Big Data Technology Adoption in Financial Services?3 Customer Insight ........................................................................... 3 Regulatory Environment ................................................................ 3 Explosive Data Growth ........
Premium Business intelligence Data management Data warehouse
In the late 1970s data-flow diagrams (DFDs) were introduced and popularized for structured analysis and design (Gane and Sarson 1979). DFDs show the flow of data from external entities into the system‚ showed how the data moved from one process to another‚ as well as its logical storage. Figure 1 presents an example of a DFD using the Gane and Sarson notation. There are only four symbols: Squares representing external entities‚ which are sources or destinations of data. Rounded rectangles
Premium Data flow diagram
Introduction This report will give an overview of the aim behind collecting data‚ types of data collected‚ methods used and how the collection of the data supports the department’s practices. It will also give a brief outlook on the importance of legislation in recording‚ storing and accessing data. Why Organisations Need to Collect Data * To satisfy legal requirement: every few months there is some request from the government sector to gather‚ maintain and reports lots of information back
Premium Human rights Law Data Protection Act 1998
a p t e r 7 MANAGING DATA RESOURCES 7.1 © 2002 by Prentice Hall LEARNING OBJECTIVES • COMPARE TRADITIONAL FILE ORGANIZATION & MANAGEMENT TECHNIQUES • DESCRIBE HOW DATABASE MANAGEMENT SYSTEM ORGANIZES INFORMATION * 7.2 © 2002 by Prentice Hall LEARNING OBJECTIVES • IDENTIFY TYPES OF DATABASE‚ PRINCIPLES OF DATABASE DESIGN • DISCUSS DATABASE TRENDS * 7.3 © 2002 by Prentice Hall MANAGEMENT CHALLENGES • TRADITIONAL DATA FILE ENVIRONMENT • DATABASE APPROACH TO DATA MANAGEMENT • CREATING DATABASE
Premium Database SQL Relational model
Data Collection Methods. Introduction Data collection is the process of gathering and measuring information on variables of interest‚ in an established systematic fashion that enables one to answer stated research questions‚ test hypotheses‚ and evaluate outcomes. Data Collection Techniques include the following: Personal Interviews Conducting personal interviews is probably the best method of data collection to gain first hand information. It is however‚ unsuitable in cases where there are
Free Sampling Simple random sample Stratified sampling
CRS Web Data Mining: An Overview Updated December 16‚ 2004 Jeffrey W. Seifert Analyst in Information Science and Technology Policy Resources‚ Science‚ and Industry Division Congressional Research Service ˜ The Library of Congress Data Mining: An Overview Summary Data mining is emerging as one of the key features of many homeland security initiatives. Often used as a means for detecting fraud‚ assessing risk‚ and product retailing‚ data mining involves the use of data analysis tools
Premium Data mining Data analysis Data management
Data Warehousing Failures Eight studies of data warehousing failures are presented. They were written based on interviews with people who were associated with the projects. The extent of the failure varies with the organization‚ but in all cases‚ the project was at least a disappointment. Read the cases and prepare a one or two page discussion of the following: 1. What’s the scope of what can be considered a data warehousing failure? Discuss. 2. What generalizations apply across
Premium Data management Data mining Data warehouse
The Other Side of Data Mining Maral Aghazi – 500287851 November 10th‚2012 ITM 200 Professor Roger De Peiza "As we and our students write messages‚ post on walls‚ send tweets‚ upload photos‚ share videos‚ and “like” various items online‚ we’re leaving identity trails composed of millions of bits of disparate data that corporations‚ in the name of targeted advertising and personalization‚ are using to track our every move” (McKee‚ 2011). Data mining has become extremely prevalent in today’s society
Premium Data mining