tag :: Prediction descartes automated_model_learning

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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, und J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Seite 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
vor 4 Jahren von @samuel.kounev
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automated_model_learning
descartes
myown
performance
performance_modeling
prediction
statistical_estimation_and_machine_learning
t_full
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, und J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Seite 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
vor 4 Jahren von @simon.eismann
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automated_model_learning
descartes
myown
performance
performance_modeling
prediction
statistical_estimation_and_machine_learning
t_full
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, und J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Seite 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
vor 4 Jahren von @se-group
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automated_model_learning
descartes
myown
performance
performance_modeling
prediction
statistical_estimation_and_machine_learning
t_full
automated_model_learningdescartesmyownperformanceperformance_modelingpredictionstatistical_estimation_and_machine_learningt_full
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS
J. Grohmann, D. Seybold, S. Eismann, M. Leznik, S. Kounev, und J. Domaschka. 2020 28th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Seite 1--8. IEEE, (November 2020)Acceptance Rate: 27%.
vor 4 Jahren von @joh.grohmann
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performance
select
t_full
automated_model_learning
statistical_estimation_and_machine_learning
myown
performance_modeling
prediction
descartes
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3Incremental Calibration of Architectural Performance Models with Parametric Dependencies
M. Mazkatli, D. Monschein, J. Grohmann, und A. Koziolek. 2020 IEEE International Conference on Software Architecture (ICSA 2020), Seite 23--34. IEEE, (März 2020)
vor 4 Jahren von @se-group
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Automated_model_learning
descartes
modeling
myown
performance
prediction
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t_full
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3Incremental Calibration of Architectural Performance Models with Parametric Dependencies
M. Mazkatli, D. Monschein, J. Grohmann, und A. Koziolek. 2020 IEEE International Conference on Software Architecture (ICSA 2020), Seite 23--34. IEEE, (März 2020)
vor 4 Jahren von @joh.grohmann
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Automated_model_learning
descartes
modeling
myown
performance
prediction
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (Oktober 2017)Würzburg Software Engineering Award sponsored by infosim.
vor 5 Jahren von @andre.bauer
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Automated_model_learning
Chameleon
Online_monitoring_and_forecasting
Optimization
Performance
Prediction
Self-aware-computing
Supervised_by_Nikolas_Herbst
Telescope
Thesis_supervised_by_Andre_Bauer
Thesis_supervised_by_SE_member
descartes
se2_mastersthesis
t_studentthesis
Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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1Modeling Server Energy Consumption using Machine Learning
T. Oestreicher. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (Oktober 2016)
vor 5 Jahren von @se-group
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Automated_model_learning
Thesis_supervised_by_Joakim_vonKistowski
Performance
Power
Prediction
descartes
Power-energy_efficient_computing
Thesis_supervised_by_SE_member
Tool
se2_mastersthesis
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (Oktober 2017)Würzburg Software Engineering Award sponsored by infosim.
vor 5 Jahren von @se-group
alle anzeigen
Automated_model_learning
Chameleon
Online_monitoring_and_forecasting
Optimization
Performance
Prediction
Self-aware-computing
Supervised_by_Nikolas_Herbst
Telescope
Thesis_supervised_by_Andre_Bauer
Thesis_supervised_by_SE_member
descartes
se2_mastersthesis
t_studentthesis
Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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3Dynamic Hybrid Forecasting for Self-Aware Systems
M. Züfle. University of Würzburg, Am Hubland, Informatikgebäude, 97074 Würzburg, Germany, Master Thesis, (Oktober 2017)Würzburg Software Engineering Award sponsored by infosim.
vor 5 Jahren von @marwin.zuefle
alle anzeigen
Automated_model_learning
Chameleon
Online_monitoring_and_forecasting
Optimization
Performance
Prediction
Self-aware-computing
Supervised_by_Nikolas_Herbst
Telescope
Thesis_supervised_by_Andre_Bauer
Thesis_supervised_by_SE_member
descartes
myown
se2_mastersthesis
t_studentthesis
Automated_model_learningChameleonOnline_monitoring_and_forecastingOptimizationPerformancePredictionSelf-aware-computingSupervised_by_Nikolas_HerbstTelescopeThesis_supervised_by_Andre_BauerThesis_supervised_by_SE_memberdescartesmyownse2_mastersthesist_studentthesis
(0)
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1Automated Extraction of Palladio Component Models from Running Enterprise Java Applications
F. Brosig. Universität Karlsruhe (TH), Karlsruhe, Germany, (Juni 2009)FZI Prize "Best Diploma Thesis".
vor 5 Jahren von @se-group
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Automated_model_learning
Award
Instrumentation_profiling_and_workload_characterization
Performance
Prediction
Thesis_supervised_by_SE_member
descartes
se2_mastersthesis
t_studentthesis
Automated_model_learningAwardInstrumentation_profiling_and_workload_characterizationPerformancePredictionThesis_supervised_by_SE_memberdescartesse2_mastersthesist_studentthesis
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2Flexible Performance Predictions at Run-time
S. Eismann. HotCloudperf 2018, (April 2018)
vor 5 Jahren von @se-group
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DML
Statistical_estimation_and_machine_learning
se2_talk
Performance
Simulation
t_talk
Prediction
descartes
Automated_model_learning
Meta-models
Analytical_and_simulation-based_analysis
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5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity
S. Herrnleben, B. Zeidler, M. Züfle, C. Krupitzer, und S. Kounev. GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020, (Februar 2020)
vor 5 Jahren von @chris.krupitzer
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Application-aware
Automated_model_learning
Cloud
Internet_of_Things
Networking
Prediction
Self-adaptive-systems
descartes
t_workshop
myown
Application-awareAutomated_model_learningCloudInternet_of_ThingsNetworkingPredictionSelf-adaptive-systemsdescartest_workshopmyown
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5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity
S. Herrnleben, B. Zeidler, M. Züfle, C. Krupitzer, und S. Kounev. GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020, (Februar 2020)
vor 5 Jahren von @samuel.kounev
alle anzeigen
Application-aware
Automated_model_learning
Cloud
Internet_of_Things
Networking
Prediction
Self-adaptive-systems
descartes
t_workshop
myown
Application-awareAutomated_model_learningCloudInternet_of_ThingsNetworkingPredictionSelf-adaptive-systemsdescartest_workshopmyown
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5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity
S. Herrnleben, B. Zeidler, M. Züfle, C. Krupitzer, und S. Kounev. GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020, (Februar 2020)
vor 5 Jahren von @se-group
alle anzeigen
Application-aware
Automated_model_learning
Cloud
Internet_of_Things
Networking
Prediction
Self-adaptive-systems
descartes
t_workshop
Application-awareAutomated_model_learningCloudInternet_of_ThingsNetworkingPredictionSelf-adaptive-systemsdescartest_workshop
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5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity
S. Herrnleben, B. Zeidler, M. Züfle, C. Krupitzer, und S. Kounev. GI/ITG Workshop on Machine Learning in the Context of Communication Networks 2020, (Februar 2020)
vor 5 Jahren von @marwin.zuefle
alle anzeigen
Application-aware
Automated_model_learning
Cloud
Internet_of_Things
Networking
Prediction
Self-adaptive-systems
descartes
myown
t_workshop
Application-awareAutomated_model_learningCloudInternet_of_ThingsNetworkingPredictionSelf-adaptive-systemsdescartesmyownt_workshop
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, und S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
vor 5 Jahren von @se-group
alle anzeigen
Automated_model_learning
Performance
Prediction
Self-adaptive-systems
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_workshop
Automated_model_learningPerformancePredictionSelf-adaptive-systemsSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_workshop
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, und S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
vor 5 Jahren von @samuel.kounev
alle anzeigen
Automated_model_learning
Performance
Prediction
Self-adaptive-systems
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_workshop
myown
Automated_model_learningPerformancePredictionSelf-adaptive-systemsSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_workshopmyown
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6On Learning Parametric Dependencies from Monitoring Data
J. Grohmann, S. Eismann, und S. Kounev. Proceedings of the 10th Symposium on Software Performance 2019 (SSP'19), (November 2019)
vor 5 Jahren von @simon.eismann
alle anzeigen
Automated_model_learning
Performance
Prediction
Self-adaptive-systems
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_workshop
myown
Automated_model_learningPerformancePredictionSelf-adaptive-systemsSelf-aware-computingStatistical_estimation_and_machine_learningdescartest_workshopmyown
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5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques
J. Grohmann, S. Eismann, S. Elflein, M. Mazkatli, J. von Kistowski, und S. Kounev. 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Seite 309--322. IEEE Computer Society, (Oktober 2019)Acceptance Rate: 23.8% (29/122).
vor 5 Jahren von @samuel.kounev
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Automated_model_learning
DML
PRISMA
Performance
Prediction
Self-aware-computing
Statistical_estimation_and_machine_learning
descartes
t_full
myown
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5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

5Baloo: Measuring and Modeling the Performance Configurations of Distributed DBMS

3Incremental Calibration of Architectural Performance Models with Parametric Dependencies

3Incremental Calibration of Architectural Performance Models with Parametric Dependencies

3Dynamic Hybrid Forecasting for Self-Aware Systems

1Modeling Server Energy Consumption using Machine Learning

3Dynamic Hybrid Forecasting for Self-Aware Systems

3Dynamic Hybrid Forecasting for Self-Aware Systems

1Automated Extraction of Palladio Component Models from Running Enterprise Java Applications

2Flexible Performance Predictions at Run-time

5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

5A Concept for Crowd-sensed Prediction of Mobile Network Connectivity

6On Learning Parametric Dependencies from Monitoring Data

6On Learning Parametric Dependencies from Monitoring Data

6On Learning Parametric Dependencies from Monitoring Data

5Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques

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