them through the lifecycle of their product in an effort to save money for the consumer while profiting from their business. Two Security Vulnerabilities Hardware vulnerabilities. According to the network infrastructure diagram‚ we can see that there are 5 servers‚ 2 routers‚ 1 switcher‚ and 1 firewall. Each one of those servers is operate by a specific department‚ and all of those servers are connected to the main database server.The connection between each department’s server to
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Data Preprocessing 3 Today’s real-world databases are highly susceptible to noisy‚ missing‚ and inconsistent data due to their typically huge size (often several gigabytes or more) and their likely origin from multiple‚ heterogenous sources. Low-quality data will lead to low-quality mining results. “How can the data be preprocessed in order to help improve the quality of the data and‚ consequently‚ of the mining results? How can the data be preprocessed so as to improve the efficiency and ease
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Censored data & Truncated data Censoring occurs when an observation or a measurement is outside the range and people don’ t know the certain value. The value is always above or below the range that people set. However‚ truncated data means that because of the limits‚ such as time‚ or space‚ people lose some data. Truncation is to cut off the data. In other words‚ we have collected and use the data‚ but the data is not in the range we have. It is called censored data. We don’t use the data because
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Gender is an example of the a. ordinal scale b. nominal scale c. ratio scale d. interval scale 2. Data obtained from a nominal scale a. must be alphabetic b. can be either numeric or nonnumeric c. must be numeric d. must rank order the data 3. In a post office‚ the mailboxes are numbered from 1 to 4‚500. These numbers represent a. qualitative data b. quantitative data c. either qualitative or quantitative data d. since the numbers are sequential‚ the data is quantitative 4. A tabular
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Information Systems Management Research Project ON Data Warehousing and Data Mining Submitted in Partial fulfilment of requirement of award of MBA degree of GGSIPU‚ New Delhi Submitted By: Swati Singhal (12015603911) Saba Afghan (11415603911) 2011-2013
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Primary Data is Original data‚ this means that it has been collected by you‚ someone who has volunteered to assist you in your research‚ or by someone who is within your employ to gather this research‚ this does not include comparing results with your peers to help evaluate the accuracy of your own results‚ as this type of data has not been gathered by you‚ or have you had any part in the gathering of this information. There are a few ways in which primary data can be obtained‚ which includes surveys
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Question1 Quantitative data are measures of values or counts and are expressed as numbers (www.abs.gov.au). In other words‚ quantitative data are data about numeric variables (www.abs.gov.au). Four types of quantitative data are interval‚ nominal‚ ordinal and ratio. Firstly‚ interval scales are numeric scales in which we know not only the order‚ but also the exact differences between the values (www.mymarketresearchmethods.com). Other than that‚ interval data also sometimes called integer is measured
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Table of Contents 1. VARIABLES- QUALITATIVE AND QUANTITATIVE......................3 1.1 Qualitative Data (Categorical Variables or Attributes) ........................... 3 1.2 Quantitative Data............................................................................................... 4 DESCRIPTIVE STATISTICS.................................................6 2.1 Sample Data versus Population Data ................................................................... 6 2.2 Parameters and Statistics
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"Data Compression and Data Processing” Please respond to the following: * Explain whether or not you believe there is a discernible difference in efficiency between compressing and decompressing audio data and compressing and decompressing image data. Provide at least three reasons for your argument. There are efficiency differences between a given compressions for audio or image‚ this is due to: 1. The difference in data‚ example bmp‚ jpg‚ gif of the same image‚ mp3‚ wav‚ ogg for
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Data Warehouses and Data Marts: A Dynamic View file:///E|/FrontPage Webs/Content/EISWEB/DWDMDV.html Data Warehouses and Data Marts: A Dynamic View By Joseph M. Firestone‚ Ph.D. White Paper No. Three March 27‚ 1997 Patterns of Data Mart Development In the beginning‚ there were only the islands of information: the operational data stores and legacy systems that needed enterprise-wide integration; and the data warehouse: the solution to the problem of integration of diverse and often redundant
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