Notes on Data Warehousing and Data Mining | Loksewa CAAN 078

Q. Write a brief note on Data Warehousing and Data Mining. (5 marks)

answer:

 Data Warehouse

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process.

A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations.

The information gathered in a warehouse can be used in any of the following domains :

• Tuning Production Strategies
• Customer Analysis
• Operation Analysis

 Functions of Data Warehouse :

1. Data Extraction
2. Data Cleaning
3. Data Transformation
4. Data Loading
5. Refreshing

Data Warehouse Applications

• Financial services
• Banking services
• Consumer goods
• Retail sectors
• Controlled manufacturing

Architecture of Data Warehousing

Data Mining

Data mining refers to extracting or mining knowledge from large amounts of data. The term is actually a misnomer. Thus, data mining should have been more appropriately named as knowledge mining which emphasis on mining from large amounts of data.

It is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use.

 The key properties of data mining are

•   Automatic discovery of patterns
•  Prediction of likely outcomes
•  Creation of actionable information 
• Focus on large datasets and databases

Data mining involves six common classes of tasks:

• Anomaly detection
• Association rule learning
• Clustering 
• Classification
• Regression
• Summarization

Data Mining Applications

• Market Analysis and Management
• Corporate Analysis & Risk Management
• Fraud Detection

Architecture of Data Mining

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