1. Cube Explorer: Online Exploration of Data Cubes Jiawei Han, Jianyong Wang, Guozhu Dong, Jian Pei, Ke Wang

2. 2 Mining Guided Cube Explorer Novel Algorithms and Methods:  Faster Creation of Iceberg Cube  Predictive Gradient Analysis  Multi-dimensional Gradient Mining  Association and Sequence Cube Analysis Integrated with commercial software such as Microsoft OLE DB for DM, OLAP, RDMS, and DBMiner

3. 3 Cube Explorer Benefits For Users:  Superior performance and scalability  Saving analysis cost and time –reusable mining queries, work directly on OLAP and relational data,  Easy to use – SQL like mining, integrated with data sources  Leverage OLAP & data warehouse engines– versatile functionality and strong synergy

4. 4 Iceberg Cube Exploration Demo  Novel H-Tree Iceberg Cube Creation  Cube Computation with Complex Measures Dataset: large retail POS transaction data

5. 5 Iceberg Cube Exploration Results  3D Visualization (Scatter Plot)

6. 6 Gradient Mining Issues 1: “What products sold with ‘TV’ will significantly change profits of ‘TV’ ?” Answer: -TV profit is up 10% when sold with DVD -TV profit is down 5% when sold with VCR 2: “What are changes of housing price in Big City in 2001 comparing against 2000?” Answer: -downtown apartments go up 15% while houses in suburb go down 5%

7. 7 How to Mine Meaningful Changes? 1 Naïve and manual method  Compute two sub-cubes  Big City housing in 2000  Big City housing in 2001  Tremendous costs  Space  Time 2 Innovation Only interesting changes wanted  “gradient constraint” to capture and predict significant changes automatically

8. 8 Gradient Mining Prediction Demo  What products sold with ‘Muffins’ will change Sales of ‘Muffins’? Select ‘Muffins’ as promotion Itemset, Sales average as Measure:

9. 9 Gradient Mining: Results 1  Most profitable patterns (Ratio >1) Rule #1: cereal increases ‘muffins” avg. sales by 8%

10. 10 Gradient Mining: Results 2  Least profitable patterns (Ratio <1) Rule #1: Ice Cream reduces ‘muffins” avg. sales by 4%

11. 11 Gradient Mining: Visualization  Results plotted using 3D bar graph