The case of Crabby Bill’s

The case of Crabby Bill’s

Book Report/Review, Computer sciences and Information technology
The case of Crabby Bill’s

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Project description
The Final Project
is in two parts, Part 1, a 2 page analytical paper, and Part 2, a 3 Page executive
summary. See below for full details.
First, you will examine the problems and opportunities presented by “Big Data” from the perspective of a small
business. You will look
at the case of “Crabby Bills” to understand the complexities faced by a smaller business
when working with diverse data.
In the second part of the Final Project, you will examine a white paper from Oracle (listed in the Web readings
for this unit. You wil
l provide an executive briefing / summary of the Oracle paper.
Exceptional performance
for this assignment will include your
demonstrated ability
to incorporate many of
the concepts covered in this course, as
inputs into your analysis
for the following Case and White Paper.
Part 1
For this Final Project you will examine a business case in order to analyze the factors that may be important
when thinking about the implications of “Big Data” in an organization. Include, at a minimum, the 3 main
topics/questions as outlined below. Any o
ther observations and/or insight based upon how the case relates to
Relational Databases, Data Warehouses, Data Administration, or other topics presented in this unit are more
than welcome.
Write a 2 page Case analysis paper using the following topics /
questions to help guide your work.
5.1 The case of

Crabby Bill’s
Please look at the Case (5.1) in Chapter 5 in
Introduction to Information Systems: Enabling and
Transforming Business.
1.
A summation of the key points raised in the case.
2.
Why did Crabby
Bill’s develop multiple databases for their data? Are there any advantages in this
approach? Support your answer.
3.
What are some disadvantages of the multiple database approach (other than the disadvantages
mentioned in this case)?
Final Project
Grading Rubric
Course: IT
234
Unit:
10
Points:
120
Copyright Kaplan University
4.
Is the technology mention
ed in the case

“File MakerPro” a relational database (If you need to, do
some Internet research on the name)?
5.
Is Crabby Bill’s only managing structured data? Explain.
6.
How was the data in separate databases (probably access and spreadsheets) integrated i
nto File
Maker?
Part 2
Managing structured data is challenging and critically important for any organization, but adding unstructured
data to the mix can seem overwhelming.
To get you started, make sure you have read the Oracle white paper from this
week’s Web readings. Then,
undertake other targeted Web research
(Search Big Data).
You need to give the president of your company an executive overview of the rationale, purposes, and benefits
of making the use of “Big Data” a priority for the organization. Provide specific details of the benefits and the
challenges that the corporation
might experience when moving to a “Big Data” orientation.
Provide a 3 page executive briefing / summary document covering the key points in the main document.
Your job is to motivate the executive(s) to read the main document.

If by chance you are uns
ure of how to structure the document or what to include in an executive
briefing/summary, it will be a great idea to search the key terms (executive briefing/summary)
and read a bit on how to do it well.
It is up to you to determine exactly how to struct
ure your summary and what to include, but it may be a good
idea to cover most of the main points in the document.
Use language that makes it easy for the non

IT executive to understand.
1.
What Makes Big Data Different?
2.
What are the capabilities necessar
y for a Big Data Implementation?
Follow the links in this section of the main document (or search the terms) to get a better overview of some of
the key technologies that provide these capabilities:
Hadoop
Cloudera
NoSql
MapReduce]
a.
Discuss Storage and
Management, Database Capability, Processing Capability Data Integration
Capability, and Statistical Analysis Capability.

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3.
Big Data Architecture
a.
Traditional Information Architecture Capabilities
b.
Adding Big Data Capabilities
Final Project
Grading Rubric
Course: IT
234
Unit:
10
Points:
120
Copyright Kaplan University
c.
An Integrated Information Architecture
d.
Making Big Data Architecture Decisions
4.
Key Drivers to Consider
5.
Big Data Best Practices
a.
Align Big Data with Specific Business Goals
b.
Ease Skills Shortage with Standards and Governance
c.
Optimize Knowledge Transfer
with a Center of Excellence
d.
Top Payoff is Aligning Unstructured with Structured Data
e.
Plan Your Sandbox For Performance
f.
Align with the Cloud Operating Model
Formatting parameters/expectations:
At least 3 full pages in length not counting the title p
age include a title page, double space, font size 12.
Arial, Courier, and Times New Roman are generally acceptable.
Includes a highly developed viewpoint, purpose and exceptional content.
Demonstrates superior organization, is well ordered, logical and unified
Ensure you have an introduction, body, and conclusion.
Free of grammar and spelling errors.
No evidence of plagiarism and quoted material makes up any more than 10% of the final con
tent.
Writing should be original. Well

paraphrased material that is cited in text and on your References page
is acceptable.
Use the APA style for all citations.
Include a minimum of two references in your paper; references may be from the text books for
the
course.
Directions for Submitting Your Assignment:
Compose your complete Final Project in a Microsoft Word document and save it as IT 234

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Sampling Designs

Sampling Designs
Sampling designs fit into one of two categories: probability or non-probability. Probability designs use random selection. Random selection means that every member of the sample has an equal chance of being selected. Your textbook explains four common probability sampling designs: simple random sampling, systematic sampling, stratified random sampling, and cluster sampling.

Non-probability designs do not include random selection. Your textbook explains four common non-probability sampling designs: convenience sampling, purposive sampling, quota sampling, expert sampling, and snowball sampling.

Assignment:

1. Select one sampling design in the probability category and one sampling design in the non-probability category and write a brief description of each of the two sampling designs you selected.
2. For each sampling design, explain how using that design could either adhere to or violate the justice principle. Explain how and why. Be specific.

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equal opportunity in a just society

equal opportunity in a just society
1. Do you think that there should be equal opportunity in a just society? What would you mean by this phrase? Do you think that it is a realizable ideal? Describe John Stuart Mill’s concept of utilitarianism. Do you see any advantages or disadvantages of applying this to our society?

2. If psychology were to be an exact, or to use Mill’s phrase, “a perfect” science, then specific human acts could be accurately predicted. Would a prediction be accurate if the person about to act becomes aware of the prediction prior to the act itself? Does the fact that a prediction can be known in advance disprove the possibility of predicting accurately or is that fact just one more antecedent condition? Thoroughly explain your view.

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Future of Parole

Future of Parole
In developing this essay, you should include a discussion of the goals and the primary and latent functions of parole; the rationale for the shift from guidelines- based sentencing to determinate sentencing; the effectiveness of parole programs including a discussion of the impact of gangs on program effectiveness; and finally, the public and political pressures which impact the future of parole.

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Page Ranking Algorithms

Page Ranking Algorithms
Project description
Page Ranking Assignment
Deliverables:
A 500-700 word paper single-spaced submitted to the Page Ranking Turnitin assignment.
Description:
Chapter 3 in “9 Algorithms” discusses how search engines have an “uncanny knack for returning extremely relevant results”. The author discusses the “Hyperlink Trick”, the “Authority Trick”, and “Random Surfer Trick”.
DecorMyEyes.com showed up as one of the first links on Google’s search results for online eyeglasses in late 2010, but for all the wrong reasons. After the NY Times published “A Bully Finds a Pulpit on the Web” (http://www.nytimes.com/2010/11/28/business/28borker.html?_r=1) Google quickly modified its secret sauce. Read the NY Times article and BetaBeat’s followup: “Brooklyn Scammer, DecorMyEyes.com, Will See Jail Time”,(http://www.betabeat.com/2011/05/13/brooklyn-scammer-decormyeyes-com-will-see-jail-time/).
There is a business in search engine optimization (SEO). To get a practical view of how web sites can increase their rating and avoid problems check:
What Is Google PageRank? A Guide For Searchers & Webmasters (//blog.hubspot.com/blog/tabid/6307/bid/45/The-Importance-of-Google-PageRank-A-Guide-For-Small-Business-Executives.aspx)
On-Page Factors: (moz.com/learn/seo/on-page-factors)
July 2011 UltraNoir article about Panda, Google’s updated indexing/ranking algorithm (http://www.ultranoir.com/en/#!/blog/web_3.0/google_panda).
In your paper :
Briefly explain each of the page ranking techniques MacCornick describes. For each trick indicate what property the trick was supposed to measure or problem the trick solved. (Check with a friend or parent to see if your description makes sense to someone else.)
Discuss which of the tricks was fooled into giving DecorMyEyes a high page ranking by Google.
If the UltraNoir article doesn’t provide pertinent techniques, propose a modification to Google’s ranking algorithm to solve the type of problem that DecorMyEyes situation presented. (MacCormick alluded to the problem on page 35 of his book.)
Discuss one positive factor and one negative SEO factor from Vaughn Aubuchon’s site.
Explain the relationship between page ranking and “googlebombing”.
On the midterm exam there will be a question which will allow you to answer a question about Freakonomics or mug shots online and their relationship to Google’s page ranking algorithm. The question might ask about the relationship between the calculation of page rankings and the ads which appear as a results of a name search on Google or the effect of page rankings on commerce.
A Freakonomics podcast dealt with a surprising phenomenon: searches for “Latanya Sweeney” brought up an ad, “Latanya Sweeney, Arrested?” Searching for Tanya Sweeney” did not. Read the transcript (http://www.freakonomics.com/2013/04/08/how-much-does-your-name-matter-full-transcript/) or listen to the recording on iTunes (there is a link from the transcript. The ame of the episode is “How Much Does Your Name Matter?” (If you are pressed for time, you can start reading the transcript where Latanya Sweeney says, “Sure I’m Latanya Sweeney.” and stop when the transcript reads, “ANNOUNCER: From WNYC and APM, American Public Media: This is Freakonomics Radio. Here’s your host, Stephen Dubner.” If you listen to the podcast on iTunes this means that you will be listening to half the program.)
On a related note, a New York Times article Mugged by a Mug Shot Online described how web sites whose business depended on people paying to have their mug shots taken down were adversely affected by a change in Google’s ranking algorithm. If you have hopes for gainful employment after graduation this article may be of interest.
It’s not needed for this assignment, but there is a very informative and insightful 2009 interview with Matt Cutts in Bloomberg Businessweek about how Google minimizes Web Spam and spot checks its search results. The article is “Matt Cutts: How Google Deals With Web Spam” (http://www.businessweek.com/the_thread/techbeat/archives/2009/10/matt_cutts_goog.html).

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