2. Outlines
Introduction
What is Big Data?
How Much Data really Exist?
Literature Review
4Vs of Big Data
Proposed System
System Architecture
Big Data mining Framework
Hadoop Framework
Big Data Challenges and solution
Conclusion
4. Interesting Facts
The volume of business data worldwide, across all companies, doubles every
1.2 years (was 1.5 years)
Daily 2500 quadrillion of data are produced and more than 90 percentage of
data are produced within past two years.
A regular person is processing daily more data than a 16th century individual
in his entire life
In the last years cost of storage and processing power dropped significantly
Bad data or poor data quality costs US businesses $600 billion annually
Facebook processes 10 TB of data every day / Twitter 7 TB
Google has over 3 million servers processing over 2 trillion searches per year
in 2012 (only 22 million in 2000)
6. “Big Data is the frontier of a firm's ability to
store, process, and access (SPA) all the
data it needs to operate effectively, make
decisions, reduce risks, and serve
customers.”
-- Forrester
7. “Big Data is the frontier of a firm's ability to
store, process, and access (SPA) all the data
it needs to operate effectively, make
decisions, reduce risks, and serve
customers.”
-- Forrester
8. “Big data is the data characterized by 3
attributes: volume, variety and velocity.”
-- IBM
9. “Big data is the data characterized by 3
attributes: volume, variety and velocity.”
-- IBM
10. Big Data is not about the size of the data,
it’s about the value within the data.
11. What is …… ?
Data Mining
‣ computational process of discovering patterns in
large data sets
Big Data
The term Big data is used to describe a massive
volume of both structured and unstructured data
that is so large that it's difficult to process using
traditional database and software techniques.
12. ‘Big Data’ is similar to ‘small data’, but bigger
…but having data bigger it requires different approaches:
Techniques, tools and architecture
…with an aim to solve new problems
…or old problems in a better way
13. How much Data does exist?
2.5 quintillion bytes of data are created EVERY DAY
IBM: 90 percent of the data in the world today were produced
with past two years
Forms of Data????
Examples : Boing Jet, Scientific Data, Sensor Data, Internet
Data,
14.
15. Literature Review
Data has grown tremendously.
This large amount of data is beyond the software tools to
manage.
Exploring the large volume of data and extracting useful
information and knowledge is a challenge, and sometimes, it is
almost infeasible.
Most people don’t know what to do with all data that they
already have
17. Huge Data with heterogeneous and diverse dimensionality
‣ represent huge volume of data
Autonomous sources with distributed and decentralized control
‣ main characteristics of Big Data
Complex and evolving relationships
18. 4 Vs of Big Data
Volume
• Data
quantity
Velocity
• Data Speed
Variety
• Data Types
Veracity
• Authenticity
19. Proposed System:
Identify relationships between different idea
Capable of handling Huge volume of Data
Uses distributed parallel computing with help of Hadoop
Provides platform for process data in different dimensions and summarized
results.
system architecture is to be flexible enough that the components built on top
of it for expressing the various kinds of processing tasks can tune it to
efficiently run these different workloads.
System will process these data within reasonable cost and time limits.
23. Big Data Mining framework
Big Data Mining Platform
Dig Data Semantics and Application Knowledge
I. Information Sharing and Data Privacy
II. Domain and Application Knowledge
Big Data Mining Algorithm
I. Local Learning and Model Fusion for Multiple
Information Sources
II. mining from Sparse, Uncertain, and Incomplete Data
III. Mining Complex and Dynamic Data
25. Challenges
Location of Big Data sources- Commonly Big Data are
stored in different locations
Volume of the Big Data- size of the Big Data grows
continuously.
Hardware resources- RAM capacity
Privacy- Medical reports, bank transactions
Having domain knowledge
Getting meaningful information
26. Solutions
Parallel computing programming
An efficient platform for computing will not have
centralized data storage instead of that platform
will be distributed in big scale storage.
Restricting access to the data
27. Advantages:
Fast response
Extract useful information
Prediction of required data from large amount of data.
Savour of better results in the form of visualization.
28. Conclusion
We have entered an era of Big Data. Through better analysis of the large
volumes of data that are becoming available, there is the potential for
making faster advances in many scientific and improving the profitability and
success of many enterprises by using technologies like hadoop ,pig and so on.
Proposed system will fully serviceable across a large variety of application
domains, and therefore not cost-effective to address in the context of one
domain alone.
Furthermore, this system will provide fully transformative solutions, and will
be address naturally for the next generation of industrial applications. We
must support and encourage this proposed framework towards addressing
these technical challenges of unstructured data, if we are to achieve the
promised benefits of Big Data.
Notas del editor
Sourcessssssssss
Social network
Satellite data
Geographical data
Live streaming data
