student education

1. Data Warehouse

10. CASE STUDY : HOTEL DATABASE

19. Need for Data Warehousing:
As there is an increasing demand for fast analysis of information, data warehouse
(DW) came up as a solution for this. The current operational systems are unable to
cater this demand because of the following reasons:
1. Online historical data is unavailable.
2. Data required for analysis is present in various operational systems.
3. Query performance is very poor as a result of which operational performance is
also very poor.
4. The operational DBMS designs are not sufficient for decision support.

25. Architecture of Data Warehousing

39. DATA MINING

40. Data Mining
Introduction:
The activity of analyzing various data from different aspects and presenting it into
meaningful information which can be utilized to promote sales and revenues or reduce
costs or both is referred to as data mining. It is also known as ‘knowledge or data
discovery”. The various analytical tools which are employed for the analysis of data are
data mining software. A user can analyze the data from various perspectives, group
them according different classifications and identify various existing relationships with
the help of these software. In technical terms the process of identifying the various
trends and correlations among the number of fields in huge relational database is
known as data mining. Data mining also known as knowledge Discovery in Databases
(KDD), refers to the nontrivial extraction of implicit, previously unknown and potentially
useful information from data stored in database.

41. Data Mining
• Search for relationships and global patterns
that exists in large databases but hidden
among the vast amount of data
• Analysis of data is main concern of data
mining along with the implementation of
various software for determining the various
trends and patterns in the available data

42. Data Sources for Data Mining
1 .Databases – Tables , Records and Fields ( rows
and columns)
2.Data Warehouse data –Data from various sources
are combined into one comprehensive and easily
manipulated database
Common accessing methods –queries, analysis and
reporting
3.Transactional Data – transactions like customer
purchase ,flight booking , users click on web page
data

43. Data Mining Process
1. Data Collection
2. Feature extraction & data cleaning
3. Analytical process and algorithm

44. Data Mining Process

45. Data Mining Technique
• Data Visualization
• Cluster Analysis
• Decision Trees
• Neural Networks etc.

46. Data Ming Techniques

47. BUSINESS INTELLIGENCE

48. Business Intelligence (BI)
• Converting large amount of corporate data
through processing and analysis into useful
information and knowledge thereby triggering
some profitable proactive business action or
decision
• BI environment is made up of business models ,
data models , ETL tools needed to transform and
organize the data into useful information and
knowledge for storage and further analysis

49. Business and Data Model
• Business model explains business process ,
flow and connection between processes and
data models used by them
• Data model is all about representing the data
and relations between them with
specifications .Data model explains data
structure . Logical model gives more details
like entities ,attributes and their relationship.

50. BI Environment