Big Data Big Data Paradigm: Paradigm: Analysis, Analysis, Application and Application and Challenges Challenges Name : Uyoyo Edosio 13 th Research Seminar Workshop University of Bradfor
Dec 24, 2014
Big Data Paradigm: Big Data Paradigm: Analysis, Application Analysis, Application
and Challengesand Challenges
Name : Uyoyo Edosio
13th Research Seminar Workshop
University of Bradfor
2
Introduction
What is Big Data?
Big Data Application
I
II
IV
Big Data AnalysisIII
3
8
14
10
Challenges Associated with Big DataV 18
ConclusionVI 21
3
IntroductionSection 1
IDC estimates the volume of digital data will grow 40% to 50% per year. By 2020, IDC predicts the number will have reached 40,000 EB, or 40 Zettabytes (ZB). The world’s information is doubling every two years. By 2020 the world will generate 50 times the amount of information and 75 times the number of information containers.
Data Trends
Data Trends
Data Trends
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Definition f Big Data
Section 2
What is Big Data?The most accepted definition of Big Data is in terms of 3 characteristics, variety, velocity and Volume (3 V’s):
Variety : depicts its heterogeneous nature
Velocity : represent the pace to which data is acquired
Volume: illustrates the size of data.
More recently another v has been proposed its called “veracity”
Difference between Big Data and Traditional Data
Unlike traditional datasets which have corresponding predefined characteristics (Such as Char, int, Varchar), Big Data sets are in form of:Structured :eg Transaction details, bank account history,Unstructured: eg Tweets, Facebook Messages,
These features in addition to its 3V characteristics makes it impossible to analyze big data on traditional relational database
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Big Data AnalysisSection 3
How do we Analyse Big Data? Typically the process of managing data include processing,
Storage and Analytics. Before now a typical RDMS could serve all these purposes at once, but due too the nature of Big data this model has changed as follows:
Big Data Processing and Storage
Algorithm for Big Data Analytics
Machine Learning Clustering Algorithm Distributed learning Algorithm
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Application of Big Data
Section 4
Application of Big Data Predictive Analytics: Using data to predict trends and
patterns.
This is applied in supply chin to forecast furture demands on a product
Descriptive Analytics: Use of historical data to explain a business. This is associated with Business intelligence, it can be applied in order to gain understanding of consumer behavior
Prescriptive Analytics: using data to suggest optimal solution. Applied in inventory systems to predict inventory level
Government Electronic Campaign Crime prediction and prevention Predict economic trends
Healthcare Predict Outbreak Health monitoring and intervention
Travel & Transportation Customer analytics and loyalty
marketing Capacity & pricing optimization Predictive maintenance optimization Location Based Services
Consumer Products
Optimized promotions effectiveness
Micro-market campaign management
Real-time demand forecast
Energy and Utilities Distribution load forecasting and
scheduling Create targeted customer offerings Condition-based maintenance Enable customer energy
management Smart meter analytics
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ChallengesSection 1
Challenges aheadInvade User's privacyProduction of Noisy DataReal time is a real problemThe Missing Skills triangleBig data tools infancy
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ConclusionSection 5
ConclusionsBig data is a Phenomena,is a Methodology.Big data might be a Challenge,but also is a Cha
nceI recommend that more there is need for
more urgent research on stable hard ware systems and computational algorithms to manage and produce insights at optimum. As data growth is a going concern
Thanks...
Any Questions 😁?