Intelligence from Social Media Data: Network Analytics meets Text Mining Killian Thiel Tobias Kötter Dr. Michael Berthold Dr. Rosaria Silipo Phil Winters Killian.Thiel@uni-konstanz.de Tobias.koetter@uni-konstanz.de Michael.Berthold@uni-konstanz.de Rosaria.Silipo@KNIME.com Phil.Winters@KNIME.com Copyright © 2012 by KNIME.com AG all rights reserved Revision: 120403F page 1 Table of Contents Creating Usable Customer Intelligence from Social Media Data: Network Analytics meets Text
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Learning Journal Reflective Comments: Thursday, 08, September 2016 This day, I learnt how to sample data (using either random sampling or cluster sampling). This has enable me to know why two samples that have the characteristics of a population are most probably different but produce the same result. I also learnt about the two types of data-qualitative data or factor and quantitative data or numeric. Now I know why numerical technique are applied to numeric value as they are numbers and cannot
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The relationship between data and information is an interconnected one. Data is raw facts such as phone numbers or addresses, and information is the organization of these raw facts into a meaningful manner. The information may be well organized on a report or table and yet not always be meaningful to all people. There are different ways to arrange data to make it meaningful for different people. For example, one person might be satisfied with information that shows him or her towns in which their
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say that data and information do not have the same meaning although we use them interchangeably. When you receive data you have to put it through some steps in order for it to be considered information to be used by an organization or company. First you have to acquire the data. There are various ways to receive opinions of customers. This can be done by posting on social websites, emailing or having a customer respond to a survey after a telephone call with the corporation. After the data is received
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and office through various THINGS but IoT is a concept where in all these THINGS are connected to internet and communicate via internet and can be intervened or controlled through internet. They also intern generate data and can be stored on cloud for further processing or the data required for control mechanism of these THINGS may exist on cloud itself. The crux of such exercise is to achieve improved efficiency, effectiveness, accuracy and economic benefit. I chose not to tell you the whole
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analyzed, and interpreted. Once the data is collected and compared, one can draw a conclusion otherwise known as the statistic. There are two types of statistical applications in business, descriptive statistics, and inferential statistics. Descriptive statistics uses methods of organizing, summarizing, and presenting data in an informative and convenient form. Inferential statistics uses sample data to make estimates or predictions about a larger set of data (McClave et al. 2011). In business
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any data for a reasonable price. The second service is called Elastic Compute Cloud (EC2) and this helps businesses to process information. The third service provided is The Mechanical Turk which is used by other businesses for processing information and then organizing that information into useful knowledge. The third service is done with the internet and also with real people to make sure that the information is translated and organized in a way that makes sense. The most common data management
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following 2 sets of data that list football teams and quarterbacks: D = {Jets, Giants, Cowboys, 49ers, Patriots, Rams, Chiefs} Q = {Tom Brady, Joe Namath, Troy Aikman, Joe Montana, Eli Manning} 1. Using D as the domain and Q as the range, show the relation between the 2 sets, with the correspondences based on which players are (or were) a member of which team(s). (You can use http://www.pro-football-reference.com to find out this information). Show the relation in the following forms: Set of ordered
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compare customer environments with regards to memory usage for the Interactive Application running on Metaframe servers in the Hosting environment. Data Information: A sample group of servers was chosen at random and the following information was obtained from these servers using two methods. Calculations were made from the both groupings of data, each section will show the calculation methods used. As a best practice on physical machines memory utilization should not exceed 90%. The remaining
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The beginning of information flow starts with the gathering of data. There is a wide range of data to be collected in any organization. Data is collected from different departments within the organization. There is also a wide range of departments for data to be collected from, depending on the type of business. Information such as invoices, e-mails, employee performance, job promotions and openings, and paid salary can be collected from the internal offices. There is information to be gathered from
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