Submitted By
Supervise By

Problem Definition
Purpose
What is ….
Challenges with data
Big data algorithms
 How To Produce The Big Data
Big Data Characteristics
Applications of Data Mining
FILD OF BIG DATA
Variety (Complexity)
Real-time/Fast Data
Real-Time Analytics/Decision Requirement
A Single View to the Customer
What’s driving Big Data
Benefits

Big Data consists of huge modules, difficult,
growing data sets with numerous and , independent
sources. With the fast development of networking,
storage of data, and the data gathering capacity, Big
Data are now quickly increasing in all science and
engineering domains, as well as animal, genetic and
biomedical sciences. This paper elaborates a HACE
theorem that states the characteristics of the Big
Data revolution, and proposes a Big Data processing
model from the data mining view.

This requires carefully designed algorithms to
analyze model correlations between distributed sites,
and fuse decisions from multiple sources to gain a
best model out of the Big Data. Developing a safe
and sound information sharing protocol is a major
challenge. To support Big Data mining, high-
performance computing platforms are required,
which impose systematic designs to unleash the full
power of the Big Data. Big data as an emerging trend
and the need for Big data mining is rising in all
science and engineering domains.

What is …… ?
Data Mining
 computational process of discovering patterns in large data sets
Big Data
 Big data is the data characterized by 3 attributes: volume, variety and
velocity.”
 it is the term for a collection of data sets so large and complex that it becomes
difficult to process
 data has exponential growth, both structured and unstructured
Data: data is any set of characters that has been gathered and translated
for some purpose, usually analysis. It can be any character, including text and
numbers, pictures, sound, or video. If data is not put into context, it doesn't
do anything to a human or computer.

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????

Data Mining Challenges with Big Data
• 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

Data Mining Algorithm
 Decision tree induction classification algorithms
 Evolutionary based classification algorithms
 Partitioning based clustering algorithms
Hierarchical
 based clustering algorithms Hierarchical based
 clustering algorithms Hierarchical based
clustering algorithms
 Model based clustering algorithms

How To Produce The Big Data
Big Data
Types
Enterprise
Data
Transactions
Public
Data
Social
Media
Sensor
Data

Big Data Characteristics
 Data has grown
tremendously.
 Big Data starts
with large-volume,
heterogeneous,
autonomous
sources with
distributed and
decentralized
system
11

Applications of Data Mining
 Marketing
 Analysis of consumer behavior
 Advertising campaigns
 Targeted mailings
Finance
o Creditworthiness of clients
o Performance analysis of finance investments
Manufacturing
o Optimization of resources
o Optimization of manufacturing processes

Variety (Complexity)
 Relational Data (Tables/Transaction/Legacy
Data)
 Text Data (Web)
 Semi-structured Data (XML)
 Graph Data
 Social Network, Semantic Web (RDF), …
 Streaming Data
 You can only scan the data once
 A single application can be
generating/collecting many types of data
 Big Public Data (online, weather, finance,
etc)
15
To extract knowledge all these types of
data need to linked together

Real-time/Fast Data
 The progress and innovation is no longer hindered by the ability to collect
data
 But, by the ability to manage, analyze, summarize, visualize, and discover
knowledge from the collected data in a timely manner and in a scalable
fashion 16
Social media and networks
(all of us are generating data)
Scientific instruments
(collecting all sorts of data)
Mobile devices
(tracking all objects all the time)
Sensor technology and
networks
(measuring all kinds of data)

Real-Time Analytics/Decision Requirement
Customer
Influence
Behavior
Product
Recommendations
that are Relevant
& Compelling
Friend Invitations
to join a
Game or Activity
that expands
business
Preventing Fraud
as it is Occurring
& preventing more
proactively
Learning why Customers
Switch to competitors
and their offers; in
time to Counter
Improving the
Marketing
Effectiveness of a
Promotion while it
is still in Play

A Single View to the Customer
Customer
Social
Media
Gamin
g
Entertain
Bankin
g
Financ
e
Our
Known
Histor
y
Purchas
e

5 Vs of Big Data
Volume
• Data quantity
Velocity
• Data Speed
Variety
• Data Types
Veracity
• Authenticity
Value
• Statistical
• Events

What’s driving Big Data
20
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
- Optimizations and predictive analytics
- Complex statistical analysis
- All types of data, and many sources
- Very large datasets
- More of a real-time

Benefits
 Cost & management
 Economies of scale, “out-sourced” resource
management
 Reduced Time to deployment
 Ease of assembly, works “out of the box”
 Scaling
 On demand provisioning, co-locate data and compute
 Reliability
 Massive, redundant, shared resources
 Sustainability
 Hardware not owned