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# Big DataParadigm, Challenges, Analysis, and Application

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Big DataParadigm, Challenges, Analysis, and Application

Big DataParadigm, Challenges, Analysis, and Application

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• 1. Big Data Paradigm:Big Data Paradigm: Analysis,Application andAnalysis,Application and ChallengesChallenges Name : Uyoyo Edosio 13th Research Seminar Workshop University of Bradfor
• 2. 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. 3 Introduction Section 1
• 4. 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
• 5. Data Trends
• 6. Data Trends
• 7. 7 Definition f Big Data Section 2
• 8. What is Big Data? The most accepted definition of Big Data is in terms of 3 characteristics, variety, velocity andVolume (3V’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”
• 9. 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 :egTransaction details, bank account history, Unstructured: egTweets, Facebook Messages, These features in addition to its 3V characteristics makes it impossible to analyze big data on traditional relational database
• 10. 10 Big Data Analysis Section 3
• 11. How do we Analyse Big Data?  Typically the process of managing data include processing, Storage andAnalytics. 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:
• 12. Big Data Processing and Storage
• 13. Algorithm for Big Data Analytics Machine Learning Clustering Algorithm Distributed learning Algorithm
• 14. 14 Application of Big Data Section 4
• 15. 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
• 16.  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
• 17.  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
• 18. 18 Challenges Section 1
• 19. Challenges ahead Invade User's privacy Production of Noisy Data Real time is a real problem The Missing Skills triangle Big data tools infancy
• 20. 20 Conclusion Section 5
• 21. Conclusions Big data is a Phenomena,is a Methodology. Big data might be a Challenge,but also is a Chance I 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
• 22. Thanks... Any Questions ?�