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1/38 Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary Aprendizado de Máquina e Avaliação de Desempenho: uma dupla dinâmica engajada da teoria à prática Edmundo de Souza e Silva 1 Universidade Federal do Rio de Janeiro 1 Programa de Engenharia de Sistemas e Computação, COPPE 2015 E. de Souza e Silva Semana PESC

Aprendizado de Máquina e Avaliação de Desempenho: uma ... · Aprendizado de Máquina e Avaliação de Desempenho: uma dupla dinâmica engajada da teoria à prática Edmundo de

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1/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Aprendizado de Máquina e Avaliação deDesempenho:

uma dupla dinâmica engajada da teoria àprática

Edmundo de Souza e Silva1

Universidade Federal do Rio de Janeiro1Programa de Engenharia de Sistemas e Computação, COPPE

2015

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation and Machine Learnig: TheDynamic Duo

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Preliminaries

What is Computer System Modeling and Analysis?What is Machine Learning?Can we take advantage of both areas?What problems we address?Is this useful in practice?

E. de Souza e Silva Semana PESC

3/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Preliminaries

What is Computer System Modeling and Analysis?What is Machine Learning?Can we take advantage of both areas?What problems we address?Is this useful in practice?

E. de Souza e Silva Semana PESC

3/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Preliminaries

What is Computer System Modeling and Analysis?What is Machine Learning?Can we take advantage of both areas?What problems we address?Is this useful in practice?

E. de Souza e Silva Semana PESC

3/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Preliminaries

What is Computer System Modeling and Analysis?What is Machine Learning?Can we take advantage of both areas?What problems we address?Is this useful in practice?

E. de Souza e Silva Semana PESC

3/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Preliminaries

What is Computer System Modeling and Analysis?What is Machine Learning?Can we take advantage of both areas?What problems we address?Is this useful in practice?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation

Modeling and analysis is an essential ingredient of thedesign process of most systemsDevising new systems: generally needs analysis of itsperformance

What are the advantages of the new architecture?which scheduling policies to use?what speed to operate servers?On what conditions can the system efficiently operate

We want to predict behavior ofan algorithma protocola new computer architecturecustomers accessing some system...

We want to perform tradeoff analysis

E. de Souza e Silva Semana PESC

5/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation

Modeling and analysis is an essential ingredient of thedesign process of most systemsDevising new systems: generally needs analysis of itsperformance

What are the advantages of the new architecture?which scheduling policies to use?what speed to operate servers?On what conditions can the system efficiently operate

We want to predict behavior ofan algorithma protocola new computer architecturecustomers accessing some system...

We want to perform tradeoff analysis

E. de Souza e Silva Semana PESC

5/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation

Modeling and analysis is an essential ingredient of thedesign process of most systemsDevising new systems: generally needs analysis of itsperformance

What are the advantages of the new architecture?which scheduling policies to use?what speed to operate servers?On what conditions can the system efficiently operate

We want to predict behavior ofan algorithma protocola new computer architecturecustomers accessing some system...

We want to perform tradeoff analysis

E. de Souza e Silva Semana PESC

5/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Performance Evaluation

Modeling and analysis is an essential ingredient of thedesign process of most systemsDevising new systems: generally needs analysis of itsperformance

What are the advantages of the new architecture?which scheduling policies to use?what speed to operate servers?On what conditions can the system efficiently operate

We want to predict behavior ofan algorithma protocola new computer architecturecustomers accessing some system...

We want to perform tradeoff analysis

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Examples

including research work ofour group

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters

Assume that the MTBF of a single disk is 300.000 hours.Probabilidade that a failure occurs in one disk unit duringone hour = 3.33310−6. (in one month: 0.0023942)Question: if you have 1000 disks, what is the probabilitythat one disk fails in one month?Answer: 0.90902Question: if you have 100.000 disks, what is the probabilitythat one disk fails in one hour? (common in disc clusters)Answer: 0.99966 −→ you WILL have a disk failedsomewhere in your cluster!!!

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters

Assume that the MTBF of a single disk is 300.000 hours.Probabilidade that a failure occurs in one disk unit duringone hour = 3.33310−6. (in one month: 0.0023942)Question: if you have 1000 disks, what is the probabilitythat one disk fails in one month?Answer: 0.90902Question: if you have 100.000 disks, what is the probabilitythat one disk fails in one hour? (common in disc clusters)Answer: 0.99966 −→ you WILL have a disk failedsomewhere in your cluster!!!

E. de Souza e Silva Semana PESC

7/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters

Assume that the MTBF of a single disk is 300.000 hours.Probabilidade that a failure occurs in one disk unit duringone hour = 3.33310−6. (in one month: 0.0023942)Question: if you have 1000 disks, what is the probabilitythat one disk fails in one month?Answer: 0.90902Question: if you have 100.000 disks, what is the probabilitythat one disk fails in one hour? (common in disc clusters)Answer: 0.99966 −→ you WILL have a disk failedsomewhere in your cluster!!!

E. de Souza e Silva Semana PESC

7/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters

Assume that the MTBF of a single disk is 300.000 hours.Probabilidade that a failure occurs in one disk unit duringone hour = 3.33310−6. (in one month: 0.0023942)Question: if you have 1000 disks, what is the probabilitythat one disk fails in one month?Answer: 0.90902Question: if you have 100.000 disks, what is the probabilitythat one disk fails in one hour? (common in disc clusters)Answer: 0.99966 −→ you WILL have a disk failedsomewhere in your cluster!!!

E. de Souza e Silva Semana PESC

7/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters

Assume that the MTBF of a single disk is 300.000 hours.Probabilidade that a failure occurs in one disk unit duringone hour = 3.33310−6. (in one month: 0.0023942)Question: if you have 1000 disks, what is the probabilitythat one disk fails in one month?Answer: 0.90902Question: if you have 100.000 disks, what is the probabilitythat one disk fails in one hour? (common in disc clusters)Answer: 0.99966 −→ you WILL have a disk failedsomewhere in your cluster!!!

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

8/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

8/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

8/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

8/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Disk clusters in Data Centers

Data centers consume a lot of powerQuestion: How can we reduce power consumption?Note: not all data in a large data center is accessedsimultaneously→ disks are not used all the time.Question: can we reduce power consumption by puttingsome disks to sleep?Answer: YES, but there are tradeoffs to investigate.What are the tradeoffs?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.

E. de Souza e Silva Semana PESC

9/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Another example

2 movie theaters competing with each other. They showthe same movieCustomers that arrive to see movie choose one of thetheaters with equal probability (theaters are identical)Question: after some time:( ) both theathers will receive approx. same amount ofcustomers (and make approx. same amount of money( ) one theater will get much more customers (make muchmore money) than the otherQuestion: can we calculate the probability that one theatermakes more than D dollars than the other?Answer: YES.After a long time there is a 80% chance one of the theatersgot more than 20.000 customers than the other!

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Modeling Cycle

the real system the model

λ1 + δλ2/δy = z

mentalabstraction

the future

measurementexperimentation

prediction

modelrefinement

the objectives

estimation

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Machine Learning

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Big Data

Large amount of data produced and consumed everydaysocial networksonline video streamingmicroblogginggenome informationmeasurements

How to obtain insights from data?What can we learn from the data?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Machine Learning

What is

Murphy:Set is methods that can automatically detectpatterns in data

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Big Data

Large amount of data produced and consumed everydaysocial networksonline video streamingmicroblogginggenome informationmeasurements

How to obtain insights from data?

data insight?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Machine Learning

What isMurphy:Set is methods that can automatically detect patterns in data

Uncovered patterns→:

predict future dataperform decision makingplanning

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Machine Learning

What isMurphy:Set is methods that can automatically detect patterns in data

Uncovered patterns→:

predict future dataperform decision makingplanning

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Obtaining insights from traces

Machine learning = automatic pattern recognitionPerformance evaluation = model building and analysismachine learning tools can help to solve performanceevaluation problems (and vice versa)

traces recommendations

models(performance evaluation)

machine learning

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

17/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Time Series

Given a time series, how to parameterize model to predictfuture values?

inferring customer behaviormodeling network channel lossesmodeling trafficgenerating workload...

Note: we have traces of time series of one or morevariables.Is there a structure behind the data?

E. de Souza e Silva Semana PESC

18/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Summary

Recall from Performance EvaluationMachine Learning

the real system the model

λ1 + δλ2/δy = z

mentalabstraction

the future

measurementexperimentation

prediction

modelrefinement

the objectives

estimation

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Summary

Recall from Performance EvaluationMachine Learning

the real system the model

λ1 + δλ2/δy = z

mentalabstraction

the future

lots of dataBIGDATA

prediction

the objectives

estimation

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Projects of our Group

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Distance Learning Initiative

P&D - servicevideoaula@RNP

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Distance Learning Initiative

More than 800 videolectures (approximately 40-90 minuteseach)Technology completely developed at the universityCEDERJ Computer Science course started in 2005It has been a service of the RNP since 2011:Videoaula@RNPDesigns started as a research project (CNPq - FAPERJprojects) and later made into a product (supported byRNP) and transfered to RNP.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Distance Learning Initiative

More than 800 videolectures (approximately 40-90 minuteseach)Technology completely developed at the universityCEDERJ Computer Science course started in 2005It has been a service of the RNP since 2011:Videoaula@RNPDesigns started as a research project (CNPq - FAPERJprojects) and later made into a product (supported byRNP) and transfered to RNP.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Distance Learning Initiative

More than 800 videolectures (approximately 40-90 minuteseach)Technology completely developed at the universityCEDERJ Computer Science course started in 2005It has been a service of the RNP since 2011:Videoaula@RNPDesigns started as a research project (CNPq - FAPERJprojects) and later made into a product (supported byRNP) and transfered to RNP.

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Distance Learning Initiative

More than 800 videolectures (approximately 40-90 minuteseach)Technology completely developed at the universityCEDERJ Computer Science course started in 2005It has been a service of the RNP since 2011:Videoaula@RNPDesigns started as a research project (CNPq - FAPERJprojects) and later made into a product (supported byRNP) and transfered to RNP.

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

The Service videoaula@RNPExample

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

The Service videoaula@RNPExample

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Videoaula@RNP ServiceUsage

Daily access: almost 7,000 accesses in one day (Feb)Reached more than 110,000 accesses in one month

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project supported by GOOGLE

Project

An Intelligent Recommendation System based on VideoLectures for Distance Education

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project supported by Google

Empresa investe em estudos académicos do...... ao comportamento de alunos de videoaulas

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project supported by Google

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project supported by Google

Reportagem Folha do estado de São Paulo, 10 de Junhode 2015http://www1.folha.uol.com.br/tec/2015/06/

1633848-google-dara-bolsas-de-mestrado-e-

doutorado-em-computacao-no-brasil.shtml

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

28/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

28/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

28/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

28/38

Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Project: Recommendation System forVideolecturesOur Objectives

Develop an Intelligent Recommendation System based onVideoLectures for Distance EducationResearch Goals:

To adapt the videolecture material according to individualuser’s needs.Automatically make suggestions to each student inrealtime:

additional written material on the subjectadditional explanation from short videosadditional exercises

Give important feedback to faculty involved on each class

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

The System

Facial Features

webcam

EEG (NeuroSky/Emotiv)

Filter

videolecture

Facial expressionclassifier

Sync

hron

izer

Loggenerator

RECOMMENDER

reco

mm

enda

tion

s

Affectiva EDA/GSR

Filter

IR webcam

Pupil Diameter

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Startup - Measurements and Planning

TGR - Tecnologia em Gestão e Planejamento de Redes

Measurements and Analysis

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Communications of the ACM - November 2012Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Impact of Home Networks on End-to-End Performance(HomNets 2010)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Broadband Internet Performance: A View From theGateway (ACM/SIGCOMM 2011)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Speed Measurements of Residential Internet Access(IEEE/PAM 2012)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Measuring Home Broadband Performance(Communications of the ACM - November 2012)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"The Realities of Home Broadband (CACM Nov 2012)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Mixture Models of Endhost Network Traffic (Infocom 2013)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Predicting user dissatisfaction with Internet applicationperformance at end-hosts (Infocom 2013)Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Parceria Universidade/Empresa

Assunto científico de interesse internacional:Artigos recentes têm sido publicados em veículosinternacionais de renome (2012, 2013)"much remains to be done to improve our understanding ofbroadband services"Transferência de novas técnicas desenvolvidas no LANDpara TGRDesenvolvimento teórico aliado a experimentação emcampoGanhos para a sociedade: tópico de interesse paraformuladores de políticas públicas e consumidoresPesquisa de ponta: Tese de mestrado 2015: Caracterização emodelos para avaliar o desempenho de redes de acesso residencialbaseados em aprendizado de máquina .

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga. Garantia de Qualidade→ economia derecursosSoftware para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutenção Redução de custosSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redePlanejamento futuro→ economia de recursosConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do cliente Planejamento→ economiade recursosEntender o comportamento do usuário de banda larga

E. de Souza e Silva Semana PESC

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Produtos Iniciais

Ferramenta de medição e diagnóstico da qualidade dabanda larga.Software para diagnosticar problemas de instalação emanutençãoSoftware para dimensionamento da capacidade da redeConhecer o tráfego do clienteEntender o comportamento do usuário de banda larga

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Projeto

Implementação do software no firmware da INTEL(gateway embedded solution)

INTELImplementação do software no firmware em escala global

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Gateway Inteligente

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Research/Development in our Group

Fault Tolerance is EssencialPerformance always matterStuart FeldmanACM’s Software System Award, Vice-President Eng.Google

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Research/Development in our Group

Fault Tolerance is EssencialPerformance always matterStuart FeldmanACM’s Software System Award, Vice-President Eng.Google

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

Research/Development in our Group

Fault Tolerance is EssencialPerformance always matterStuart FeldmanACM’s Software System Award, Vice-President Eng.Google

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Preliminaries Performance Evaluation Examples Modeling Machine Learning Projects Startup - TGR Summary

FIM

OBRIGADO!PERGUNTAS?

www.abc.org.br/~edmundowww.land.ufrj.br/~edmundo

E. de Souza e Silva Semana PESC