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The Impact of Big Data

In: Computers and Technology

Submitted By pa7ng
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The Impact of Big Data
By

Ijaaz Lagardien
Group 3A
214167542

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Contents
Plagiarism Declaration ....................................................................................................................................... 3
Abstract ............................................................................................................................................................. 4
Keywords ........................................................................................................................................................... 4
Introduction ....................................................................................................................................................... 4
Semi-structured data ......................................................................................................................................... 5
What is structured data ................................................................................................................................. 5
What is semi-structured data ........................................................................................................................ 5
Types of semi-structured data....................................................................................................................... 5
Unstructured data ............................................................................................................................................. 6
What is unstructured data............................................................................................................................. 6
Types of unstructured data ........................................................................................................................... 6
Uses of big data ................................................................................................................................................. 7
Conclusion ......................................................................................................................................................... 8
References ......................................................................................................................................................... 9

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Plagiarism Declaration
I know that plagiarism means taking and using the ideas, writings, works or inventions of another as if they were my own. I know that plagiarism not only includes verbatim copying, but also the extensive use of another person’s ideas without proper acknowledgement (which includes the proper use of quotation marks). I know that plagiarism covers this sort of use of material found in textual sources and from the Internet. I acknowledge and understand that plagiarism is wrong. I understand that my research must be accurately referenced. I have followed the rules and conventions concerning referencing, citation and the use of quotations as set out in the Departmental Guide.
This assignment is my own work, or my group’s own unique group assignment. I acknowledge that copying someone else’s assignment, or part of it, is wrong, and that submitting identical work to others constitutes a form of plagiarism.
I have not allowed, nor will I in the future allow, anyone to copy my work with the intention of passing it off as their own work.

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Abstract
This paper summarises the purpose and use of big data. It defines terms used in the world of big data and makes comparisons between unstructured and semi-structured data. Furthermore it shows how big data makes an impact on our daily lives by looking at where it is being used and how certain companies use it.
Additionally a conclusion will be drawn based on how big data does indeed impact our daily lives.

Keywords
Volume, velocity, variety, variability, veracity, Hadoop, semi-structured data, unstructured data.
Data mining, JSON, XML, email, OEM.

Introduction
Big data is a term used to describe sets of data that are so large or complex that it cannot be processed by traditional data processing applications. Thus making accounting software such as QuickBooks, for example, inadequate when dealing with large business’ accounts.
Alternatively big data can also be defined as a large amount of data that is either structured or unstructured and inundates a business on a daily basis [1] . Big data is typically characterised by the “three Vs” as its common known. The ‘V’s are known as volume, velocity and variety respectively.
The volume characteristic refers to the amount of data companies collect from platforms such as social media, business transactions and machine-to-machine data. Technologies such as Hadoop have eased the predicament of storing such large amounts of data.
Velocity refers to how big data is always available in real time. No matter how large the volume of the data may be.
Lastly variety refers to how big data is available in all formats such as images, videos, audio, and text just to name a few.
However many experts believe that big data has two more characteristics namely variability and veracity
Variability refers inconsistency of data at certain moments for example when something is trending on social media. Veracity to the varying quality of data collection and how this affects data analysis.
As aforementioned big data can be either semi-structured or unstructured and therefore I will be making a comparison between semi-structured and unstructured data and determine how it impacts our lives based on where big data is used.

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Semi-structured data
To start my comparison of semi-structured and unstructured data I will first be looking semi-structured data and its characteristics. Before we look at semi-structured data it is important to define structured data.

What is structured data
Structured data is defined as information such as text files displayed in a titled column and row format which can easily be processed by data mining tools. A great visual example of this would be a filing cabinet where all information is labelled and easily accessible.

What is semi-structured data
Semi-structured data or data model is defined as information from numerous different sources with different but interrelated properties. Semi-structured data may be irregular or incomplete but has a structure that changes rapidly and unpredictably.
Semi-structured data occurs with more frequency than before. Since emergence of HTML we have been looking at semi-structured data ever since we have had a form of an internet connection, because semistructured data is essentially a form of sharing information on the internet.

Types of semi-structured data
XML and other markup languages, email and EDI are all forms of semi-structured data. The Object Exchange
Model (OEM) was created prior to XML and is also considered a semi-structured data model. Furthermore
JSON or JavaScript Object Notation is an open standard format of semi-structured data that uses human readable text.

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Unstructured data
What is unstructured data
Unstructured is defined as data that does not follow a specific format in the world of big data. Untrusted data makes up 80 percent of all data [2], and it is available to us in all different formats. It is everywhere we always see it but, we always know it is there and available but many of us do not know what we are essentially looking at is unstructured data.

Types of unstructured data
Unstructured data is everywhere we see and come into contact with it every day and many people had no idea that all of the following are examples of unstructured data.
1. Satellite images
a. Weather reports
b. Google earth
2. Photographs and videos
a. Surveillance videos and images
3. Social media data
a. Tweets and Facebook posts
4. Mobile data
a. Text messages and location data
5. Website content
a. Any data from Instagram and YouTube.

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Uses of big data
Big data is being used daily by various people around the world here are some examples of where and how big data is being used daily to make a difference in our lives.
1. Understanding and targeting customers
a. Many companies and retailers alike use platforms like social media to look at customer needs and wants. For example during the recent Galaxy S7 launch in Barcelona, Spain Samsung reintroduced the micro SD card slot on its flagship devices stating they “listened” to customers. This indicates a possible monitoring social media criticism of its previous generation Galaxy S6. They analysed the big data and used it to their advantage to create a phone customers wanted.
2. Understanding and optimizing business’ transactions
a. Big data is used to optimise business’ transactions. For example companies can determine how much stock of a product to ship out based on the analyses of web search trends social media trends and weather patterns.
3. Improving health care and public health
a. Big data allows us to decode DNA strings in minutes, allows us to find cures and better predict disease patterns. Big data has also been used to monitor premature babies and by analyses an algorithm has been created which can predict infections 24 hours before they happen.
4. Improving sports performance
a. The use of video analyses for sports teams have improved the way they perform. Technology has reached a level where rugby teams for example can analyse their opposition to determine a game plan. It has gotten to the point where players can analyse their individual performances and know where to improve for the next game.
5. Improving science and research
a. Big data brings new possibilities in the field of sciences too. For example the CERN Large
Hadron Collider in Switzerland has 65000 processors to analyse 30 petabytes of data from
150 data centres around the world to analyse data it gathers on experiments to unlock the secrets of our universe.
6. Optimizing machine and device performance
a. Big data analyses allows machines to become smarter and more independent for example big data is being used in the Google self-driving car research. Big data is also used to optimise the performance of computers and data warehouses.
7. Improving security and law enforcement
a. Big data is used extensively by law enforcement agencies like the NSA, by monitoring websites and various social media platforms to stop terrorist plots. Others use big data to prevent cyber-attacks while police use big data to catch criminals and predict criminal activities, while credit card companies analyse big data to determine credit card fraud.
8. Improving cities and countries
a. Big data is used in many ways to improve our cities and countries. For example cities can optimise traffic flow by looking at social media and weather reports. On the N1 there are
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electronic boards that update drivers on weather conditions, accidents and travel time to a certain point.
9. Financial Trading
a. Big data is being used daily in the HFT (High frequency trading) aspect of financial trading.
Big algorithms are used to make decisions. In this day and age data algorithms used to make financial trading look at news websites and social media to determine whether or not to buy or sell.
10. Weather services
a. Technology has advanced so much so that mobile phones these days have weather sensors built into them. For example the Samsung Galaxy SIV uses a barometer, thermometer and light meter to determine temperatures and various other weather conditions.

Conclusion
We are presented with data each and every day and many of us will not know if the data is structured or not.
But in reality there is no real theory to determine whether or not data, big or small, is unstructured or not but we can definitely say that 100 percent of data is made up of 20 percent semi-structured data and 80 percent unstructured data [2].
Big data will always be a part of our daily lives and through various types of research I can state that yes big data impacting our lives on a daily basis and we don’t always realise it. Whether it be from a medical standpoint based on how an algorithm was created to determine an infection up to 24 hours before it happens in premature babies and the way law enforcement uses it to keep us safe from threats or simply the way retailers use it to deliver products with features we ask for rather than features they want us to like.
In conclusion big data is always evolving based on how technology improves and it will continue to make a, sometimes inconspicuous, but huge impact in our lives.

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References
[1] SAS.com, “What is big data,” [Online]. Available: http://www.sas.com/en_us/insights/big-data/whatis-big-data.html. [Accessed 8 March 2016].
[2] J. Hurwitz, A. Nugent, F. Halper and M. Kaufman, Big Data for Dummies.
[3] The Economist, “Data, data everywhere,” 25 February 2010. [Online]. Available: http://www.economist.com/node/15557443. [Accessed 6 March 2016].
[4] R. Primmer, “Structured vs unstructured,” [Online]. Available: http://www.robertprimmer.com/blog/structured-vs-unstructured.html. [Accessed 7 March 2016].
[5] S. Software, “Stuctured and unstructured data what is it?,” [Online]. Available: http://www.sherpasoftware.com/blog/structured-and-unstructured-data-what-is-it/. [Accessed 7
March 2016].
[6] N. Akkok, P. Halvorsen, A. M. Keller and V. Goebal, “Semi-structured data and XML,” Ellen MuntheKaas.
[7] D. Suciu, “Introduction to Semistructured data and XML,” in Database Management Systems, 3 ed.,
Wisconsin, Wisconsin: Univeristy of Wisconsin, 1996.
[8] C. Coronel, S. Morris, P. Rob and Keeley Crocket, Database Principles Fundementals of Design,
Implementation and Management, 2 ed., CENGAGE Learning, 2013, p. 866.
[9] Brightplanet.com, “Structured vs unstrucured,” 28 June 2012. [Online]. Available: http://www.brightplanet.com/2012/06/structured-vs-unstructured-data/. [Accessed 8 March 2016].
[10] B. Marr, “The Awesome ways Big Data Is Used Today To Change Our World,” Linkedin, 13 Novemebr
2013. [Online]. Available: https://www.linkedin.com/pulse/20131113065157-64875646-theawesome-ways-big-data-is-used-today-to-change-our-world. [Accessed 8 March 2016].
[11] Tutorials Point, “Relational Data Model,” [Online]. Available: http://www.tutorialspoint.com/dbms/relational_data_model.htm. [Accessed 8 March 2016].

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