Presentation is loading. Please wait.

Presentation is loading. Please wait.

Progress Report on Continuous Data Stream Management  Mining Frequent Itemsets over Data Streams  Music Virtual Channel Presented by: Dr. Yi-Hung Wu.

Similar presentations


Presentation on theme: "Progress Report on Continuous Data Stream Management  Mining Frequent Itemsets over Data Streams  Music Virtual Channel Presented by: Dr. Yi-Hung Wu."— Presentation transcript:

1 Progress Report on Continuous Data Stream Management  Mining Frequent Itemsets over Data Streams  Music Virtual Channel Presented by: Dr. Yi-Hung Wu Date: 2004/11/3 Post-Excellence Project Subproject 6

2 Continuous Data Stream Management /9 A data stream is a continuous, massive, and rapid data flow… 1 Data Stream Data Stream Processor Synopsis Report Query Archive Buffer Time Storage Accuracy Sampling

3 Continuous Data Stream Management /9 Mining Data Streams  Various data formats  Relational tuples, transactions, sequences, trees, graphs, …  Various interesting patterns  Association, trends, clustering, outliers, classification, …  Various interestingness measures  Support, confidence, lift, chi-square value, entropy, … 2

4 Continuous Data Stream Management /9 Mining Data Streams  Landmark model  Time-decaying model  Sliding-window model 3

5 Continuous Data Stream Management /9 Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding-Window 4 Discounting table Potential count

6 Continuous Data Stream Management /9 Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding-Window 5

7 Continuous Data Stream Management /9 Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding-Window 6  Our contributions  The time-sensitive sliding-window model  Fast mining and discounting method mining time > maintenance time  Self-adjusting discounting table selective adjustment vs. naïve adjustment  Accuracy guarantees No false dismissal  false alarm rate No false alarm  false dismissal rate

8 Continuous Data Stream Management /9 Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding-Window 7

9 Continuous Data Stream Management /9 Music Virtual Channel: Framework 8 … 1 1 N N 2 2 … Data sources Internet Overlay network V.C. player V.C. player Clustering engine Clustering engine Filtering engine Filtering engine Interface Profile monitor Profile monitor Cluster monitor Cluster monitor Channel monitor Channel monitor

10 Continuous Data Stream Management /9 Music Virtual Channel: Implementation 9 … 1 1 N N 2 2 … Music Databases Internet V.C. player V.C. player Clustering engine Clustering engine Filtering engine Filtering engine Music stream simulator Internet Music feature database


Download ppt "Progress Report on Continuous Data Stream Management  Mining Frequent Itemsets over Data Streams  Music Virtual Channel Presented by: Dr. Yi-Hung Wu."

Similar presentations


Ads by Google