Data mining With Big Data

Presented By:

Sandip B. Tipayle Patil

Under the Guidance of

Prof. Y.N.Patil

DEPARTMENT OF COMPUTER ENGINEERING

DR. BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY

Lonere.

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

Introduction

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)

What is

“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

“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

“Big data is the data characterized by 3

attributes: volume, variety and velocity.”

-- IBM

“Big data is the data characterized by 3

attributes: volume, variety and velocity.”

-- IBM

Big Data is not about the size of the data,

it’s about the value within the data.

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.

‘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

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,

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

Giant Elephant

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

4 Vs of Big Data

Volume

• Data quantity

Velocity

• Data Speed

Variety

• Data Types

Veracity

• Authenticity

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.

Gap due to Lack of analysis

System Architecture:

Hadoop framework :

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

Big Data mining Framework

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

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

Advantages:

Fast response

Extract useful information

Prediction of required data from large amount of data.

Savour of better results in the form of visualization.

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.