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: juergen.bernard@gris.informatik.tu-darmstadt.de

http://www.gris.tu-darmstadt.de/home/members/bernard/index.de.htm

Outline

1. Motivation

2. Practical Approach

3. Outlook

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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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