2015
Barnes, David C.
Image Clustering of Scientific Databases Presentation
05.10.2015, (LA-UR-15-27725).
Abstract | Links | BibTeX | Tags: cinema, clustering
@misc{Barnes2015,
title = {Image Clustering of Scientific Databases},
author = {David C. Barnes},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Image_Clustering_of_Scientific_Databases.pdf
http://datascience.dsscale.org/wp-content/uploads/2016/08/Data_Science_Cinema_Poster_Full.png},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to provide image clustering of scientific databases. },
note = {LA-UR-15-27725},
keywords = {cinema, clustering},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to provide image clustering of scientific databases.
2014
Widanagamaachchi, Wathsala; Bremer, Peer-Timo; Sewell, Christopher; Lo, Li-ta; Ahrens, James; Pascucci, Valerio
Data-Parallel Halo Finding with Variable Linking Lengths Proceedings Article
In: 2014, (LA-UR-14-23700).
Abstract | Links | BibTeX | Tags: clustering, cosomology, halo
@inproceedings{Widanagamaachchi2014,
title = {Data-Parallel Halo Finding with Variable Linking Lengths},
author = {Wathsala Widanagamaachchi and Peer-Timo Bremer and Christopher Sewell and Li-ta Lo and James Ahrens and Valerio Pascucci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Data-ParallelHaloFindingWithVariableLinkingLenghts.pdf},
year = {2014},
date = {2014-11-01},
abstract = {State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.},
note = {LA-UR-14-23700},
keywords = {clustering, cosomology, halo},
pubstate = {published},
tppubtype = {inproceedings}
}
State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.
2009
Woodring, Jonathan; Shen, Han-Wei
Multiscale time activity data exploration via temporal clustering visualization spreadsheet Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 15, no. 1, pp. 123–137, 2009.
Abstract | Links | BibTeX | Tags: animation, clustering, filter banks, K-means, time histogram, time-varying, transfer function, visualization spreadsheet, Wavelet
@article{woodring2009multiscale,
title = {Multiscale time activity data exploration via temporal clustering visualization spreadsheet},
author = {Jonathan Woodring and Han-Wei Shen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/MultiscaleTimeActivityDataExplorationViaTemporalClusteringVisualizationSpreadsheet.pdf},
year = {2009},
date = {2009-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {15},
number = {1},
pages = {123--137},
publisher = {IEEE},
abstract = {Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multiscale time series curve sets. The time curves are clustered so that data of similar activity are grouped together at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet, where she is able to select temporal clusters of data points and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.},
keywords = {animation, clustering, filter banks, K-means, time histogram, time-varying, transfer function, visualization spreadsheet, Wavelet},
pubstate = {published},
tppubtype = {article}
}
Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multiscale time series curve sets. The time curves are clustered so that data of similar activity are grouped together at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet, where she is able to select temporal clusters of data points and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.
2008
Santos, Emanuele; Lins, Lauro; Ahrens, James; Freire, Juliana; Silva, Claudio T
A first study on clustering collections of workflow graphs Book Chapter
In: Provenance and Annotation of Data and Processes, pp. 160–173, Springer, 2008, (LA-UR-10-02553).
Abstract | Links | BibTeX | Tags: clustering, workflow
@inbook{Santos2008,
title = {A first study on clustering collections of workflow graphs},
author = {Emanuele Santos and Lauro Lins and James Ahrens and Juliana Freire and Claudio T Silva},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AFirstStudyOnClusteringCollectionsOfWorkflowGraphs.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Provenance and Annotation of Data and Processes},
pages = {160--173},
publisher = {Springer},
abstract = {As work ow systems get more widely used, the number of work ows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of work ow and provenance graphs. We propose two diㄦent representations for these graphs and present an experimental evaluation, using a collection of 1,700 work ow graphs, where we study the trade-oóof these representations and the ectiveness of alternative clustering techniques.},
note = {LA-UR-10-02553},
keywords = {clustering, workflow},
pubstate = {published},
tppubtype = {inbook}
}
As work ow systems get more widely used, the number of work ows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of work ow and provenance graphs. We propose two diㄦent representations for these graphs and present an experimental evaluation, using a collection of 1,700 work ow graphs, where we study the trade-oóof these representations and the ectiveness of alternative clustering techniques.
: . .
1.
Barnes, David C.
Image Clustering of Scientific Databases Presentation
05.10.2015, (LA-UR-15-27725).
@misc{Barnes2015,
title = {Image Clustering of Scientific Databases},
author = {David C. Barnes},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/08/Image_Clustering_of_Scientific_Databases.pdf
http://datascience.dsscale.org/wp-content/uploads/2016/08/Data_Science_Cinema_Poster_Full.png},
year = {2015},
date = {2015-10-05},
abstract = {This presentation summarizes summer work to provide image clustering of scientific databases. },
note = {LA-UR-15-27725},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
This presentation summarizes summer work to provide image clustering of scientific databases.
2.
Widanagamaachchi, Wathsala; Bremer, Peer-Timo; Sewell, Christopher; Lo, Li-ta; Ahrens, James; Pascucci, Valerio
Data-Parallel Halo Finding with Variable Linking Lengths Proceedings Article
In: 2014, (LA-UR-14-23700).
@inproceedings{Widanagamaachchi2014,
title = {Data-Parallel Halo Finding with Variable Linking Lengths},
author = {Wathsala Widanagamaachchi and Peer-Timo Bremer and Christopher Sewell and Li-ta Lo and James Ahrens and Valerio Pascucci},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/Data-ParallelHaloFindingWithVariableLinkingLenghts.pdf},
year = {2014},
date = {2014-11-01},
abstract = {State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.},
note = {LA-UR-14-23700},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
State-of-the-art cosmological simulations regularly contain billions of particles, providing scientists the opportunity to study the evolution of the Universe in great detail. However, the rate at which these simulations generate data severely taxes existing analysis techniques. Therefore, developing new scalable alternatives is essential for continued scientific progress. Here, we present a dataparallel, friends-of-friends halo finding algorithm that provides unprecedented flexibility in the analysis by extracting multiple linking lengths. Even for a single linking length, it is as fast as the existing techniques, and is portable to multi-threaded many-core systems as well as co-processing resources. Our system is implemented using PISTON and is coupled to an interactive analysis environment used to study halos at different linking lengths and track their evolution over time.
3.
Woodring, Jonathan; Shen, Han-Wei
Multiscale time activity data exploration via temporal clustering visualization spreadsheet Journal Article
In: Visualization and Computer Graphics, IEEE Transactions on, vol. 15, no. 1, pp. 123–137, 2009.
@article{woodring2009multiscale,
title = {Multiscale time activity data exploration via temporal clustering visualization spreadsheet},
author = {Jonathan Woodring and Han-Wei Shen},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/MultiscaleTimeActivityDataExplorationViaTemporalClusteringVisualizationSpreadsheet.pdf},
year = {2009},
date = {2009-01-01},
journal = {Visualization and Computer Graphics, IEEE Transactions on},
volume = {15},
number = {1},
pages = {123--137},
publisher = {IEEE},
abstract = {Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multiscale time series curve sets. The time curves are clustered so that data of similar activity are grouped together at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet, where she is able to select temporal clusters of data points and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Time-varying data is usually explored by animation or arrays of static images. Neither is particularly effective for classifying data by different temporal activities. Important temporal trends can be missed due to the lack of ability to find them with current visualization methods. In this paper, we propose a method to explore data at different temporal resolutions to discover and highlight data based upon time-varying trends. Using the wavelet transform along the time axis, we transform data points into multiscale time series curve sets. The time curves are clustered so that data of similar activity are grouped together at different temporal resolutions. The data are displayed to the user in a global time view spreadsheet, where she is able to select temporal clusters of data points and filter and brush data across temporal scales. With our method, a user can interact with data based on time activities and create expressive visualizations.
4.
Santos, Emanuele; Lins, Lauro; Ahrens, James; Freire, Juliana; Silva, Claudio T
A first study on clustering collections of workflow graphs Book Chapter
In: Provenance and Annotation of Data and Processes, pp. 160–173, Springer, 2008, (LA-UR-10-02553).
@inbook{Santos2008,
title = {A first study on clustering collections of workflow graphs},
author = {Emanuele Santos and Lauro Lins and James Ahrens and Juliana Freire and Claudio T Silva},
url = {http://datascience.dsscale.org/wp-content/uploads/2016/06/AFirstStudyOnClusteringCollectionsOfWorkflowGraphs.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Provenance and Annotation of Data and Processes},
pages = {160--173},
publisher = {Springer},
abstract = {As work ow systems get more widely used, the number of work ows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of work ow and provenance graphs. We propose two diㄦent representations for these graphs and present an experimental evaluation, using a collection of 1,700 work ow graphs, where we study the trade-oóof these representations and the ectiveness of alternative clustering techniques.},
note = {LA-UR-10-02553},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
As work ow systems get more widely used, the number of work ows and the volume of provenance they generate has grown considerably. New tools and infrastructure are needed to allow users to interact with, reason about, and re-use this information. In this paper, we explore the use of clustering techniques to organize large collections of work ow and provenance graphs. We propose two diㄦent representations for these graphs and present an experimental evaluation, using a collection of 1,700 work ow graphs, where we study the trade-oóof these representations and the ectiveness of alternative clustering techniques.