1. Continuous Data Stream Processing
Make Lab

2. Application and Environment
The framework and objectives
Querying data streams
Mining data streams
An integrated system
Music virtual channel
V.C. player
user profile (moving objects)
user queries (continuous queries)
Clustering engine
virtual channel (collaborative decision)
Filtering engine
favorite channel (content-based decision)
Indexing server
Decoder server
UniGrid
Camera
Sensor
Video-on-demand

3. Research Directions (1/2)
Mechanisms for processing continuous queries
Temporal query processing
Continuous query processing over event streams based on approximate matching mechanisms
Continuously matching episodes for triggering episode rules over event streams
Spatial query processing
Continuous clustering moving objects in multiple space
Monitoring heterogeneous kNN moving objects considering location-independent attributes
Aggregate query processing
An efficient method for processing multiple continuous Top-k queries
Maintaining moving sums over data streams

4. Research Direction (2/2)
One-pass mining algorithms for data streams
Frequent tree pattern mining
Discovering frequent tree patterns over data streams
Mining frequent subtrees over data streams using closed subtrees
Frequent Itemset mining
Processing multiple queries of finding frequent itemsets over multiple data streams
Mining frequent itemsets from data streams with a time-sensitive sliding window
A novel hash-based approach for mining frequent itemsets over data streams with memory consideration under landmark model
Frequent sequence mining
Mining serial episode rules with successor lag times over multiple data streams

5. Continuous query processing over event streams based on approximate matching mechanisms
UniGrid
UniGrid Portal
Queries
Answers
Filtering Engine
User

6. Processing multiple queries of finding frequent itemsets over multiple data streams

7. Multiple queries of frequent itemsets over multiple streams
UniGrid A
UniGrid Portal B C D
Queries
Answers

8. An efficient method for processing multiple continuous Top-k queries…
<O1, 15><O2, 13><O3, 16>… N1
<O1, 2><O2, 5><O3, 4>…
<O1, 5>
<O2, 7>
<O3, 9> …
Multiple continuous top-1 queries:
<O3, 16> N2

9. Frequent tree patterns mining over data streams
STMer