Data Mining for Business Intelligence

1.                  Define data mining. Why are there many different names and definitions for data mining?

Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models.

Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining.

Application Case: HP Applies Management Science Modeling to Optimize Its Supply Chain and Wins a Major Award

HP’s ground-breaking use of operations research not only enabled the-tech giant to successfully transform its portfolio program and return $500 million over a 3-year period to the bottom line, it also earned HP the coveted 2009 Edelman Award from INFORMS for outstanding achievement in operations research. “This is not the success of just one person or one team,” said Kathy Chou, Vice President of Worldwide Commercial Sales at HP, in accepting the award on behalf of the winning team. “It’s the success of many people across HP who made this a reality, beginning several years ago with mathematics and imagination and what it might do for HP.”

Q & A: Modeling & Analysis

1.  Q: List three lessons learned from modeling.
     A: This question refers to lessons found in the examples in this section, not to lessons learned from modeling in general. Many examples could be cited, among them these four:
•    DuPont learned how different approaches to rail transportation would work out.
•    The University of Virginia Health Science Center (the institution studied in the cited Rossetti and Selandari paper) learned how human couriers and mobile robots compare in making deliveries within a hospital.
•    Procter & Gamble learned the best shipping options from product sources to distribution centers.
•    American Airlines learned the optimum ascent and descent profiles for its aircraft.

Q & A Review on DSS Concepts. Methodologies, and Technologies: An Overview

1.    Q: List and describe the three major components of DSS.
       A: The three major components are: data, models, and user interface.

Data refers to the information needed to make a decision, typically stored in a database, and to how these data are organized and managed by a DBMS.

Models refer to the models used to analyze the data and predict the results of a decision, as well as to the software used to manage the use of the models in a DSS.

User interface refers to the way a manager or knowledge worker can use the system to support his or her decision making needs without having to become an expert in its technology.

Q & A Review on Decision Making, Modeling, & Support

1.     Q: What are some of the key questions to be asked in supporting decision making through DSS?
             A:
•    What are the root issues underlying the decision situation?  Do we understand the problem sufficiently to support it? 
•    How structured is the decision? Is it unstructured, semi-structured, or structured?
•    Does the decision involve judgment? To what extent?
•    What data is needed to solve the problem?
•    Can an existing tool be leveraged or reused?
•    Is a tool needed?
•    What is the implementation plan?

Q & A Review on DSS & BI

1.    Q: List the components of and explain the Business Pressures–Responses–Support model.
A: The components of the pressure-response-support model are business pressures, companies’ responses to these pressures, and computerized support. The model suggests that responses are made to counter the pressures or to take advantage of opportunities, support facilitates monitoring the environment (e.g., for opportunities) and enhances the quality of the responses.

Components of academic paper

Abstract	·         No more than 100 words ·         Summarizes arguments ·         Mentions aims/purposes ·         Mentions focus of literature ·         Mentions methods of research ·         Mentions findings ·         Mentions implications
Introduction	·         Provides background information and rationale for the research, and indicates why the topic is useful/interesting/significant ·         Presents research questions ·         Presents research aims

10 Life Lessons from Einstein

1. Follow Your Curiosity
“I have no special talent. I am only passionately curious.”

2. Perseverance is Priceless
“It’s not that I’m so smart; it’s just that I stay with problems longer.”

3. Focus on the Present
“Any man who can drive safely while kissing a pretty girl is simply not giving the kiss the attention it deserves.”

What Your I.Q. Means

116+
17 percent of the world population; superior I.Q.; appropriate average for individuals in professional occupations.

121+
10 percent; potentially gifted; average for college graduates

132+
2 percent; borderline genius; average I.Q. of most Ph.D. recipients

Top 100 Newspapers Worldwide

Top 100 Newspapers Worldwide (ranked by circulation)
Rank     Newspaper     Circulation     Location
1.      Yomiuri Shimbun     10,021,000     Tokyo, Japan
2.      Asahi Shimbun     8,054,000     Tokyo, Japan
3.      Sichuan Daily     8,000,000     Chengdu, China
4.      Mainichi Shimbun     3,912,000     Tokyo, Japan
5.      Bild     3,548,000     Hamburg, Germany
6.      Cankao Xiaoxi     3,183,000     Beijing, China
7.      Times of India     3,146,000     New Delhi, India
8.      Nihon Keizai Shimbun (The Nikkei)     3,053,000     Tokyo, Japan
9.      Sun     2,821,618     London, United Kingdom
10.      People's Daily     2,808,000     Beijing, China