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Visualization and Search Approaches for Time-Oriented Scientific Primary Data
A WGL-TIB Project
Jürgen Bernard
Technische Universität DarmstadtFachgebiet Graphisch-Interaktive Systeme (GRIS)Visual Analysis GroupFraunhoferstraße 564283 DarmstadtGermany
Tel.: +49 (6151) 155 – 666Fax: +49 (6151) 155 – 669
Email: [email protected]
http://www.gris.tu-darmstadt.de/home/members/bernard/index.de.htm
Outline
1. Motivation
2. Practical Approach
3. Outlook
10.04.23 | Interactive-Graphics Systems | Visual Search and Analysis Group | Jürgen Bernard | 2
Trends Content-based search and presentation available for many document types
Text documents Digital image, video, audio, etc.
Repositories for new kinds of non-textural documents Like scientific primary data PANGAEA, PsychData, Dryad, ELEXIR, KoLaWiss
Information overload: need for visual retrieval and data exploration
1 Motivation
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Time-oriented scientific primary data Massive amounts of time-oriented scientific primary data that may be valuable
for future research Heterogenity of standards in different research disciplines Exploration of scientific primary data repositories currently restricted to “meta-
search”
1 Motivation
PANGAEA PanPlot Tool. (http://doi.pangaea.de/10.1594/PANGAEA.330147)
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Development of a Visual Catalog for time series data Interactive-graphic access to huge amounts of scientific primary data Combination of content-based, and meta data retrieval Explorative data analysis by searching, browsing and zooming techniques User-adaptive search methods Establish higher data transparency and deeper user comprehension
1 Motivation
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Possible Use Case Scenario
Natural scientist detects interesting curve progression
Hypothesis: curve progression indicates future event
Search for similar curve progressions in related data sets
Visual overview of the most similar data elements
Filter result set, adapt the reference example
1 Motivation
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Outline
1. Motivation
2. Practical Approach
3. Outlook
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2.1 Overview
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Back End
Front End
Bernard, J. and Brase, J. and Fellner, D. and Koepler, O. and Kohlhammer, J. and Ruppert, T. and Schreck, T. and Sens, I. A Visual Digital Library Approach for Time-Oriented Scientic Primary Data. Accepted at the 14th European Conference on Research and Advanced Technology for Digital
Libraries (ECDL), 2010
2.2 Data Import
Parse primary data Initialy: use Pangaea data files In principle: inclusion of additional repositories by customized data parsers
Define a generic time series data structure Feature based approach Store meta data as „bag of words“ Special attributes: time stamp,
parameter/unit
Defined data base schema MySQL data base Efficient data management
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2.3 Preprocessing
Problem: time series primary data may not be directly applicable for clustering, consider:Outliers, noiseMissing valuesformat of time stamps in data filesInhomogenous time quantization, timestamp compatibility
Possible approachesAggregation (binning)TransformationInterpolation
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2.4 Feature Extraction
Descriptors: representations of time series in a reduced dimensionality space
Basis for similarity measures (clustering, indexing, search) Problem: full sequence vs. sub sequence search Huge amount of descriptors published, suitable descriptor approaches:
Binning, (current approach), Discrete Fourier Transformation (DFT), Discrete Wavelet Transformation (DWT), SAX-Descriptor (symbolic representation)
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Lin, J. and Keogh, E. and Lonardi, S. and Chiu, B. : A symbolic representation of time series, with implications for streaming algorithms.Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003
2.5 Visual Catalog
Visualization of clustering results (global und local) Self-organizing Map as a basis for smart visualization of huge amounts of
time series Provide a single grid view for details (zooming) We also explore other layout algorithms
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2.5 Visual Catalog
Visual-interactive time series search Query by example, query by sketch Problem: fullsequence vs. subsequence search Colormaps for the indication of similarity List-based visualizations for result sets Save session, export results
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2.5 Visual Catalog
Meta data search Additional search functionality, combination with content-based search Search by „Bag of words“ Search by special attributes
Physical unit, example: temperature, humidity, etc. Use time stamp: define time interval and quantization
Combine multiple search operations: filtering
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Filtering
Outline
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1. Motivation
2. Practical Approach
3. Outlook
3 Outlook
Summary WGL-TIB project: visual access to scientific primary data Project start: 01/2010, project duration: 3 years Facing time-series data Feature-based descriptor approach Interfaces for visualization, browsing and search
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3 Outlook
Requirements, use-cases and evaluation model (TIB Hannover) Test visual catalog prototype with real world problems Collaboration with scientific users to capture user view User in the loop approach
Technical future work (GRIS, FhG) Establish an application prototype Similarity search operations, based on evaluation results Special user interface
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Thank you for your attention
Comments very welcome
Do you have any questions?
Acknowledgements
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Related Work
PANGAEA Publishing Network for Geoscientic & Environmental Data. (http://www.pangaea.de/)
PsychData National Repository for Psychological Research Data. (http://psychdata.zpid.de/ (in German))
Dryad Digital Repository for Data Underlying Published Works. (http://www.datadryad.org/)
ELIXIR European Life Sciences Infrastructure for Biological Information. (http://www.elixir-europe.org/)
KoLaWiss: Society for Scientic Data Processing Goettingen: Cooperative long-term preservation for research centers (in German).Project Report (2009)
PANGAEA PanPlot Tool. (http://doi.pangaea.de/10.1594/PANGAEA.330147) Bernard, J. and Brase, J. and Fellner, D. and Koepler, O. and Kohlhammer, J. and
Ruppert, T. and Schreck, T. and Sens, I.: A Visual Digital Library Approach for Time-Oriented Scientic Primary Data. Accepted at the 14th European Conference on Research and Advanced Technology for Digital Libraries (ECDL), 2010
Lin, J. and Keogh, E. and Lonardi, S. and Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, 2003
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