1) The document discusses the evolution of database technology from the 1960s to present, including primitive file processing, DBMS, relational DBMS, advanced data models, and data warehousing and mining. 2) It explains why organizations mine data from a commercial viewpoint, to gain competitive advantages through better customized services, and from a scientific viewpoint to make use of large amounts of data being collected. 3) Data mining is defined as the process of analyzing data from different perspectives to extract useful patterns and knowledge, and examples are given of what is and is not considered data mining.

1. DATAWAREHOUSING AND MINING
BY
G.RAJESH CHANDRA

2. EVOLUTION OF DATABASE TECHNOLOGY

1960s (Primitive File Processing)


1970s to early 1980s (DBMS)


Data collection, database creation, IMS and network DBMS
Relational data model, relational DBMS implementation ,SQL,
OLTP,User Interfaces.etc
1980s: to Present (Advanced Data Bases)



RDBMS, advanced data models (extended-relational, OO, deductive,
etc.)
Application-oriented DBMS (spatial, scientific, engineering, etc.)
1990s: (Advanced Data Analysis)


Data mining, data warehousing, multimedia databases, and Web
databases
2000s

Stream data management and mining

Data mining and its applications

3. WHY MINE DATA? COMMERCIAL VIEWPOINT

Lots of data is being collected
and warehoused




Web data, e-commerce
purchases at department/
grocery stores
Bank/Credit Card
transactions
Competitive Pressure is Strong

Provide better, customized services for an edge (e.g. in
Customer Relationship Management)

4. WHAT IS DATA MINING…..?

•
Data mining (sometimes called data
Discovery or Knowledge Discovery Data)
is the process of analyzing data from
different perspectives and summarizing it
into useful information.
Extraction of interesting (non-trivial,
implicit, previously unknown and
potentially useful) patterns or knowledge
from huge amount of data

5. WHY MINE DATA? SCIENTIFIC VIEWPOINT

Data collected and stored at
enormous speeds (GB/hour)






remote sensors on a satellite
telescopes scanning the skies
microarrays generating gene
expression data
scientific simulations
generating terabytes of data
Traditional techniques infeasible for raw
data
Data mining may help scientists


in classifying and segmenting data
in Hypothesis Formation

6. EXAMPLES: WHAT IS (NOT) DATA MINING?
 What is not Data
 What is Data Mining?
Mining?
– Look up phone
– Certain names are more
number in phone
directory
prevalent in certain US locations
(O’Brien, O’Rurke, O’Reilly… in
Boston area)
– Query a Web
– Group together similar
documents returned by search
engine according to their context
(e.g. Amazon rainforest,
Amazon.com,)
search engine for
information about
―Amazon‖

7. DATA MINING IS ALSO CALLED AS..?
•
•
Knowledge discovery (mining) in
databases (KDD), knowledge extraction,
data/pattern analysis, data archeology,
data dredging, information harvesting,
business intelligence, etc.
Real Time Example Gold Mining

8. DATA WARE HOUSE = COLLECTION OF DATA BASES

9. WE HAVE TO USE DIFFERENT METHODS

10. RAW DATA =DATA BASES + NOISE DATA

11. DATA SELECTION AND TRANSFORMATION

12. DATA CLEANING AND INTEGRATION

13. DATA MINING

14. PATTERN EVALUATION

15. KNOWLEDGE REPRASENTATION

16. KNOWLEDGE REPRASENTATION

17. December 26, 2013
KNOWLEDGE DISCOVERY (KDD) PROCESS
 Data
mining—core of
knowledge discovery
process
Pattern Evaluation
Data Mining
Task-relevant Data
Data Warehouse
Data Cleaning
Data Integration
Databases
Selection