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Data Stream Mining for Ubiquitous Environments
Published on Nov 16, 20123445 Views
In the data stream computational model examples are processed once, using restricted computational resources and storage capabilities. The goal of data stream mining consists of learning a decision mo
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Chapter list
Challenges in Ubiquitous Data Mining00:00
Outline02:13
Flags02:25
Motivation02:26
Problem Formulation: Network Data Model03:40
Querying Model04:11
Routing schemes04:45
The Demand for Learning05:02
Illustrative Example: Renewable Power Prediction05:54
Collaborative Forecasting: Main Idea06:59
Collaboration09:08
Local Search (1)10:25
Local Search (2)10:47
Local Search (3)10:50
Collaboration: broadcast time-stamps of similar contexts10:51
Local search: Inferring the Global Context (1)11:51
Local search: Inferring the Global Context (2)12:00
Local search: Inferring the Global Context (3)12:03
The Global Context12:08
Prediction12:16
Lessons Learned12:25
Clustering Distributed Data Streams (1)13:20
Clustering Distributed Data Streams (2)14:48
Clustering Distributed Sources of Data Streams15:17
Clustering Distributed Data Streams (3)15:44
Clustering Distributed Data Streams (4)16:27
System Overview16:42
Step 1: Local Step18:13
Local Adaptive Grid18:28
Step 2: Aggregation Step19:02
Monitoring States19:40
Frequent States22:55
Step 3: Centralized Cluster22:58
Furthest Point Clustering23:02
Illustrative Example24:36
Main Achievements25:00
Clustering Distributed Sources of Data Streams25:02
A k-means Algorithm for Evolving Data25:24
Example: Local Clustering (1)26:35
Example: Local Clustering (2)27:35
Example: Local Clustering (3)27:48
Receiving Neighbors Data28:48
Sending Data to Neighbors29:07
After 512 Iterations...29:38
Evaluation: Electrical Grid Data30:02
Lessons Learned31:16
A World in Movement31:22
The Challenges of UDM32:51
Thank you!33:37