Data_Warehousing_and_Data_Mining_Presentation.pptx
- 1. Data WareHouse and Data Mining Presented by Anil Tharu Banke Bageshwori Campus 2082/02/05
- 2. Introduction Data Warehouse • A data warehouse is a repository of information collected from multiple sources that stores historical data and provides support for decision-makers for data modeling and analysis. • The data warehouse is the core of the Business Intelligence system which is built for data analysis and reporting.
- 3. Characteristics of a Data Warehouse • Subject-Oriented – organized around key subjects • Integrated – consistent data from various sources • Time-Variant – historical data is maintained • Non-Volatile – once entered, data is not changed
- 4. Architecture of Data Warehousing • Data Sources → ETL (Extract, Transform, Load) → Data Warehouse → Data Marts → BI Tools
- 5. What is Data Mining? • Simply stated, data mining refers to extracting or mining knowledge from large amounts of data stored in databases, data warehouses, or other information repositories. • Many people treat data mining as a synonym for another popularly used term, Knowledge Discovery from Data or KDD.
- 6. Key Data Mining Techniques • Classification – e.g., spam detection • Clustering – e.g., customer segmentation • Association Rules – e.g., market basket analysis • Regression – e.g., sales forecasting • Anomaly Detection – e.g., fraud detection • Web Search Engine – google • Social and Web Network
- 7. Applications in Real Life • Retail: Inventory and customer behavior analysis • Finance: Fraud detection and risk assessment • Healthcare: Disease prediction and patient profiling • Manufacturing: Quality control and process optimization
- 8. Popular Tools and Platforms • Data Warehousing Tools: Amazon Redshift, Google BigQuery, Snowflake • Data Mining Tools: RapidMiner, Weka, KNIME, SAS, IBM SPSS, Python
- 9. Challenges in Data Warehousing and Mining • Data quality and integration • Scalability and performance • Security and privacy concerns • Skilled resource requirement
- 10. Future Trends • Cloud-based data warehousing • Integration with AI and ML • Real-time data processing • Ethical data mining and responsible AI
- 11. Conclusion • Data warehousing enables structured storage. • Data mining extracts hidden value from that data. • Together, they empower organizations to make smarter decisions.
- 12. Thank You for your attention ❤️ 💻 Do you have any Questions?