SlideShare ist ein Scribd-Unternehmen logo
1 von 15
A Framework for Clustering Evolving Data Streams Yueshen Xu Zhejiang Univ CCNT, Middleware Middle ware, CCNT, ZJU 11/05/11
S tream  P rocessing     E vent  S tream  P rocessing     C omplex  E vent  P rocessing 11/05/11 Middleware, CCNT, ZJU D ata  S tream  M ining E vent  S tream  P rocessing C omplex  E vent  P rocessing In-memory Computing Real –time Computing Big data Computing Mode Real Application SAP… Taobao Yahoo:S4 Baidu Brown&MIT We are endeavoring!
The paper itself ,[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU Watson UIUC THU UIC ,[object Object],Expert Pundit Expert Pundit !
Data Stream & Streaming Data ,[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU ,[object Object]
Principles ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU ,[object Object],[object Object],Ordered, Countable, Enumerable, Infinite, no-storage  Data Model Vital!
The Framework ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU The principle of  approximate results ,[object Object],[object Object],[object Object],Is it sophistic?
C luster  F eature  V ector ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Why are they opted for? 11/05/11 Middleware, CCNT, ZJU ,[object Object],[object Object],[object Object],Not come up by Prof. Han  et al
Pyramidal Time Frame(1) ,[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU ,[object Object],Worse Case~~
Pyramidal Time Frame(2) ,[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU Best case No redundancy ,[object Object],[object Object],[object Object],[object Object],[object Object]
Micro-Cluster(1) ------Procedure 11/05/11 Middleware, CCNT, ZJU t h h’ Micro cluster(CFV) Snapshots T
Micro-Cluster(2) ------Initialization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU Supported by the experiment Reasonable ?
Micro-Cluster(3) ------Updating ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU
Macro-Cluster(1) ------Find the approximate time stamp ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU Not user-friendly
Macro-Cluster(2) ------modified k-means ,[object Object],[object Object],[object Object],[object Object],[object Object],11/05/11 Middleware, CCNT, ZJU
[object Object],11/05/11 Middleware, CCNT, ZJU Stream Stream Stream Stream Stream Stream Stream Stream

Weitere ähnliche Inhalte

Was ist angesagt?

Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache FlinkAlbert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
Flink Forward
 
Efficient top-k queries processing in column-family distributed databases
Efficient top-k queries processing in column-family distributed databasesEfficient top-k queries processing in column-family distributed databases
Efficient top-k queries processing in column-family distributed databases
Rui Vieira
 

Was ist angesagt? (20)

5.1 mining data streams
5.1 mining data streams5.1 mining data streams
5.1 mining data streams
 
Recent progress on distributing deep learning
Recent progress on distributing deep learningRecent progress on distributing deep learning
Recent progress on distributing deep learning
 
Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache FlinkAlbert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
Albert Bifet – Apache Samoa: Mining Big Data Streams with Apache Flink
 
Efficient top-k queries processing in column-family distributed databases
Efficient top-k queries processing in column-family distributed databasesEfficient top-k queries processing in column-family distributed databases
Efficient top-k queries processing in column-family distributed databases
 
2017 nov reflow sbtb
2017 nov reflow sbtb2017 nov reflow sbtb
2017 nov reflow sbtb
 
Tuning Java Servers
Tuning Java Servers Tuning Java Servers
Tuning Java Servers
 
Realtime Data Analysis Patterns
Realtime Data Analysis PatternsRealtime Data Analysis Patterns
Realtime Data Analysis Patterns
 
Crash course on data streaming (with examples using Apache Flink)
Crash course on data streaming (with examples using Apache Flink)Crash course on data streaming (with examples using Apache Flink)
Crash course on data streaming (with examples using Apache Flink)
 
Tutorial: The Role of Event-Time Analysis Order in Data Streaming
Tutorial: The Role of Event-Time Analysis Order in Data StreamingTutorial: The Role of Event-Time Analysis Order in Data Streaming
Tutorial: The Role of Event-Time Analysis Order in Data Streaming
 
Mining big data streams with APACHE SAMOA by Albert Bifet
Mining big data streams with APACHE SAMOA by Albert BifetMining big data streams with APACHE SAMOA by Albert Bifet
Mining big data streams with APACHE SAMOA by Albert Bifet
 
Dynamic Data Center concept
Dynamic Data Center concept  Dynamic Data Center concept
Dynamic Data Center concept
 
Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on ...
Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on ...Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on ...
Matrix Factorizations at Scale: a Comparison of Scientific Data Analytics on ...
 
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduceBlock Sampling: Efficient Accurate Online Aggregation in MapReduce
Block Sampling: Efficient Accurate Online Aggregation in MapReduce
 
Deep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in SparkDeep recurrent neutral networks for Sequence Learning in Spark
Deep recurrent neutral networks for Sequence Learning in Spark
 
Real time stream processing presentation at General Assemb.ly
Real time stream processing presentation at General Assemb.lyReal time stream processing presentation at General Assemb.ly
Real time stream processing presentation at General Assemb.ly
 
The data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architecturesThe data streaming processing paradigm and its use in modern fog architectures
The data streaming processing paradigm and its use in modern fog architectures
 
Streaming Algorithms
Streaming AlgorithmsStreaming Algorithms
Streaming Algorithms
 
04 open source_tools
04 open source_tools04 open source_tools
04 open source_tools
 
Ph. D. Final Dissertation SLides
Ph. D. Final Dissertation SLidesPh. D. Final Dissertation SLides
Ph. D. Final Dissertation SLides
 
FlinkML: Large Scale Machine Learning with Apache Flink
FlinkML: Large Scale Machine Learning with Apache FlinkFlinkML: Large Scale Machine Learning with Apache Flink
FlinkML: Large Scale Machine Learning with Apache Flink
 

Ähnlich wie Stream data mining & CluStream framework

Clustering
ClusteringClustering
Clustering
Anjan Goswami
 
Comp7404 ai group_project_15apr2018_v2.1
Comp7404 ai group_project_15apr2018_v2.1Comp7404 ai group_project_15apr2018_v2.1
Comp7404 ai group_project_15apr2018_v2.1
paul0001
 
A frame work for clustering time evolving data
A frame work for clustering time evolving dataA frame work for clustering time evolving data
A frame work for clustering time evolving data
iaemedu
 
CS 542 -- Concurrency Control, Distributed Commit
CS 542 -- Concurrency Control, Distributed CommitCS 542 -- Concurrency Control, Distributed Commit
CS 542 -- Concurrency Control, Distributed Commit
J Singh
 
3 3 energy efficient topology
3 3 energy efficient topology3 3 energy efficient topology
3 3 energy efficient topology
IAEME Publication
 
3 3 energy efficient topology
3 3 energy efficient topology3 3 energy efficient topology
3 3 energy efficient topology
prjpublications
 

Ähnlich wie Stream data mining & CluStream framework (20)

Clustering
ClusteringClustering
Clustering
 
Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)Clustering for Stream and Parallelism (DATA ANALYTICS)
Clustering for Stream and Parallelism (DATA ANALYTICS)
 
Comp7404 ai group_project_15apr2018_v2.1
Comp7404 ai group_project_15apr2018_v2.1Comp7404 ai group_project_15apr2018_v2.1
Comp7404 ai group_project_15apr2018_v2.1
 
DTN
DTNDTN
DTN
 
Time Series Analysis… using an Event Streaming Platform
Time Series Analysis… using an Event Streaming PlatformTime Series Analysis… using an Event Streaming Platform
Time Series Analysis… using an Event Streaming Platform
 
Time Series Analysis Using an Event Streaming Platform
 Time Series Analysis Using an Event Streaming Platform Time Series Analysis Using an Event Streaming Platform
Time Series Analysis Using an Event Streaming Platform
 
onur-comparch-fall2018-lecture3b-memoryhierarchyandcaches-afterlecture.pptx
onur-comparch-fall2018-lecture3b-memoryhierarchyandcaches-afterlecture.pptxonur-comparch-fall2018-lecture3b-memoryhierarchyandcaches-afterlecture.pptx
onur-comparch-fall2018-lecture3b-memoryhierarchyandcaches-afterlecture.pptx
 
IC05 cours 4
IC05 cours 4IC05 cours 4
IC05 cours 4
 
Guidelines for-early-power-analysis
Guidelines for-early-power-analysisGuidelines for-early-power-analysis
Guidelines for-early-power-analysis
 
A frame work for clustering time evolving data
A frame work for clustering time evolving dataA frame work for clustering time evolving data
A frame work for clustering time evolving data
 
Automating the Hunt for Non-Obvious Sources of Latency Spreads
Automating the Hunt for Non-Obvious Sources of Latency SpreadsAutomating the Hunt for Non-Obvious Sources of Latency Spreads
Automating the Hunt for Non-Obvious Sources of Latency Spreads
 
Target Response Electrical usage Profile Clustering using Big Data
Target Response Electrical usage Profile Clustering using Big DataTarget Response Electrical usage Profile Clustering using Big Data
Target Response Electrical usage Profile Clustering using Big Data
 
C* Summit 2013: Time is Money Jake Luciani and Carl Yeksigian
C* Summit 2013: Time is Money Jake Luciani and Carl YeksigianC* Summit 2013: Time is Money Jake Luciani and Carl Yeksigian
C* Summit 2013: Time is Money Jake Luciani and Carl Yeksigian
 
Pretzel: optimized Machine Learning framework for low-latency and high throu...
Pretzel: optimized Machine Learning framework for  low-latency and high throu...Pretzel: optimized Machine Learning framework for  low-latency and high throu...
Pretzel: optimized Machine Learning framework for low-latency and high throu...
 
Performance and predictability
Performance and predictabilityPerformance and predictability
Performance and predictability
 
CS 542 -- Concurrency Control, Distributed Commit
CS 542 -- Concurrency Control, Distributed CommitCS 542 -- Concurrency Control, Distributed Commit
CS 542 -- Concurrency Control, Distributed Commit
 
Security Center.pdf
Security Center.pdfSecurity Center.pdf
Security Center.pdf
 
Semantics in Sensor Networks
Semantics in Sensor NetworksSemantics in Sensor Networks
Semantics in Sensor Networks
 
3 3 energy efficient topology
3 3 energy efficient topology3 3 energy efficient topology
3 3 energy efficient topology
 
3 3 energy efficient topology
3 3 energy efficient topology3 3 energy efficient topology
3 3 energy efficient topology
 

Mehr von Yueshen Xu

(Hierarchical) Topic Modeling_Yueshen Xu
(Hierarchical) Topic Modeling_Yueshen Xu(Hierarchical) Topic Modeling_Yueshen Xu
(Hierarchical) Topic Modeling_Yueshen Xu
Yueshen Xu
 
Summarization for dragon star program
Summarization for dragon  star programSummarization for dragon  star program
Summarization for dragon star program
Yueshen Xu
 

Mehr von Yueshen Xu (20)

Context aware service recommendation
Context aware service recommendationContext aware service recommendation
Context aware service recommendation
 
Course review for ir class 本科课件
Course review for ir class 本科课件Course review for ir class 本科课件
Course review for ir class 本科课件
 
Semantic web 本科课件
Semantic web 本科课件Semantic web 本科课件
Semantic web 本科课件
 
Recommender system slides for undergraduate
Recommender system slides for undergraduateRecommender system slides for undergraduate
Recommender system slides for undergraduate
 
推荐系统 本科课件
 推荐系统 本科课件 推荐系统 本科课件
推荐系统 本科课件
 
Text classification 本科课件
Text classification 本科课件Text classification 本科课件
Text classification 本科课件
 
Thinking in clustering yueshen xu
Thinking in clustering yueshen xuThinking in clustering yueshen xu
Thinking in clustering yueshen xu
 
Text clustering (information retrieval, in chinese)
Text clustering (information retrieval, in chinese)Text clustering (information retrieval, in chinese)
Text clustering (information retrieval, in chinese)
 
(Hierarchical) Topic Modeling_Yueshen Xu
(Hierarchical) Topic Modeling_Yueshen Xu(Hierarchical) Topic Modeling_Yueshen Xu
(Hierarchical) Topic Modeling_Yueshen Xu
 
(Hierarchical) topic modeling
(Hierarchical) topic modeling (Hierarchical) topic modeling
(Hierarchical) topic modeling
 
Non parametric bayesian learning in discrete data
Non parametric bayesian learning in discrete dataNon parametric bayesian learning in discrete data
Non parametric bayesian learning in discrete data
 
聚类 (Clustering)
聚类 (Clustering)聚类 (Clustering)
聚类 (Clustering)
 
Yueshen xu cv
Yueshen xu cvYueshen xu cv
Yueshen xu cv
 
徐悦甡简历
徐悦甡简历徐悦甡简历
徐悦甡简历
 
Learning to recommend with user generated content
Learning to recommend with user generated contentLearning to recommend with user generated content
Learning to recommend with user generated content
 
Social recommender system
Social recommender systemSocial recommender system
Social recommender system
 
Summary on the Conference of WISE 2013
Summary on the Conference of WISE 2013Summary on the Conference of WISE 2013
Summary on the Conference of WISE 2013
 
Topic model an introduction
Topic model an introductionTopic model an introduction
Topic model an introduction
 
Acoustic modeling using deep belief networks
Acoustic modeling using deep belief networksAcoustic modeling using deep belief networks
Acoustic modeling using deep belief networks
 
Summarization for dragon star program
Summarization for dragon  star programSummarization for dragon  star program
Summarization for dragon star program
 

Kürzlich hochgeladen

The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
heathfieldcps1
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
ciinovamais
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
fonyou31
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
kauryashika82
 

Kürzlich hochgeladen (20)

microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1Código Creativo y Arte de Software | Unidad 1
Código Creativo y Arte de Software | Unidad 1
 
Interactive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communicationInteractive Powerpoint_How to Master effective communication
Interactive Powerpoint_How to Master effective communication
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Disha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdfDisha NEET Physics Guide for classes 11 and 12.pdf
Disha NEET Physics Guide for classes 11 and 12.pdf
 
social pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajansocial pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajan
 
General AI for Medical Educators April 2024
General AI for Medical Educators April 2024General AI for Medical Educators April 2024
General AI for Medical Educators April 2024
 
The basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptxThe basics of sentences session 2pptx copy.pptx
The basics of sentences session 2pptx copy.pptx
 
Activity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdfActivity 01 - Artificial Culture (1).pdf
Activity 01 - Artificial Culture (1).pdf
 
fourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writingfourth grading exam for kindergarten in writing
fourth grading exam for kindergarten in writing
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
Ecosystem Interactions Class Discussion Presentation in Blue Green Lined Styl...
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy Reform
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17Advanced Views - Calendar View in Odoo 17
Advanced Views - Calendar View in Odoo 17
 
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in DelhiRussian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
Russian Escort Service in Delhi 11k Hotel Foreigner Russian Call Girls in Delhi
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 

Stream data mining & CluStream framework

  • 1. A Framework for Clustering Evolving Data Streams Yueshen Xu Zhejiang Univ CCNT, Middleware Middle ware, CCNT, ZJU 11/05/11
  • 2. S tream P rocessing  E vent S tream P rocessing  C omplex E vent P rocessing 11/05/11 Middleware, CCNT, ZJU D ata S tream M ining E vent S tream P rocessing C omplex E vent P rocessing In-memory Computing Real –time Computing Big data Computing Mode Real Application SAP… Taobao Yahoo:S4 Baidu Brown&MIT We are endeavoring!
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 8.
  • 9.
  • 10. Micro-Cluster(1) ------Procedure 11/05/11 Middleware, CCNT, ZJU t h h’ Micro cluster(CFV) Snapshots T
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.