Data Warehousing & Mining Notes
Subject Overview
Data Warehousing & Mining covers data warehouse fundamentals and OLAP/
Subject at a glance
4
Units
31
Topics
5
Credits
4
Infographics
Unit-wise Syllabus
4 units — open any unit for detailed topic-wise notes, diagrams and important exam questions
- Need for data warehousing
- operational vs informational data stores
- data warehouse characteristics/role/structure
- cost of warehousing
- OLAP vs OLTP
- OLAP operations
Purpose
Day-to-day transactions
Analysis and decisions
Data
Current, detailed
Historical, summarised
Queries
Short, simple, many
Complex, fewer
Design
Normalised
Star or snowflake schema
Study guide
This unit explains why organisations build data warehouses, operational versus informational stores, warehouse characteristics, role and structure, costs, OLAP versus OLTP and OLAP operations. Learn the OLAP operations: roll-up, drill-down, slice, dice and pivot.
- Design/technical/implementation considerations
- data preprocessing (summarization, cleaning, transformation)
- concept hierarchy
- multidimensional data model
- star/snowflake/fact-constellation schemas
- data warehouse architecture and design
- OLAP three-tier architecture
- cube computation
- attribute-oriented induction
Time dimension
Day, month, year
Product dimension
Name, category, brand
Store dimension
City, region
Customer dimension
Age, segment
Study guide
You study warehouse design and implementation, data preprocessing, concept hierarchies, the multidimensional model, star, snowflake and fact-constellation schemas, architecture, OLAP three-tier architecture, cube computation and attribute-oriented induction. Draw a star schema for a sales data warehouse.
- Market basket analysis
- Apriori algorithm
- mining multilevel association rules
- association-to-correlation analysis
- constraint-based association mining
- classification by decision tree
- attribute selection measures
Support
Transactions with A and B / all transactions
Confidence
Support(A and B) / Support(A)
Lift
Confidence(A ⇒ B) / Support(B)
Lift > 1 means positive association
Apriori property
Every subset of a frequent itemset is frequent
Study guide
You study market basket analysis, the Apriori algorithm, multilevel association rules, correlation analysis, constraint-based mining and decision tree classification with attribute selection measures. Work an Apriori example by hand.
- Prediction techniques
- classifier accuracy
- cross-validation
- bootstrap
- boosting
- bagging
- clustering algorithm classification
- selecting the right data mining technique
- data visualization
Classification
Decision trees, Naive Bayes
Prediction
Regression
Clustering
k-means, hierarchical
Association
Apriori, market basket
Ensembles
Bagging, boosting
Study guide
You study prediction techniques, classifier accuracy, cross-validation, bootstrap, boosting, bagging, clustering algorithms, choosing the right technique and data visualisation. Know why cross-validation gives a more reliable accuracy estimate.
Why Data Warehousing & Mining Matters
Data warehousing and mining are the foundation of business intelligence and data science, a fast-growing career area with high demand.
How to Prepare for Data Warehousing & Mining
- Practise Apriori and decision tree problems.
- Learn the difference between OLTP and OLAP and between star and snowflake schemas.
- Try a mining tool such as Weka.
Data Warehousing & Mining: Frequently Asked Questions
What is the difference between OLTP and OLAP?
OLTP handles day-to-day transactions, while OLAP supports complex analysis of historical data.
What is the Apriori algorithm?
Apriori finds frequent itemsets and association rules by repeatedly extending itemsets that meet a minimum support.