# Presentation: 914659

## A Paradigm for Space Science Informatics

**Kirk D. Borne**

- George Mason University and QSS Group Inc., NASA-Goddard
- kborne@gmu.edu   or   kirk.borne@gsfc.nasa.gov

**Timothy E. Eastman  ****(presenter)**

- QSS Group Inc., NASA-Goddard
- eastman@mail630.gsfc.nasa.gov

- and

## What is Informatics?

**Informatics is the discipline of structuring, storing, accessing, and distributing information describing complex systems.**
- Examples:
- Bioinformatics
- Geographic Information Systems (= Geoinformatics)
- New! Space Science Informatics
- Common features of X-informatics:
- Basic data unit is defined
- Common community tools operate on data units
- Data-centric and Information-centric approaches
- Data-driven science
- X-informatics is key enabler of scientific discovery in the era of large data science

- 5/26/2006

## X-Informatics Compared

_**Discipline X**_
- Bioinformatics
- Geoinformatics
- Space Sc. Informatics

_**Common Tools**_
- BLAST, FASTA
- GIS
- CDAWeb, Bayes Inference, Cross Correlations, Principal Components

_**Data Unit**_
- Gene Sequence
- Points, Vectors, Polygons
- Time Series, Event Lists, Catalogs, Object Parameters

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## Data-Information-Knowledge-Wisdom

- T.S. Eliot (1934):
*“**Where is the wisdom we have lost in knowledge?*
*  **Where is the knowledge we have lost in information?”*

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## Key Role of Data Mining

- Data Mining = ***an information extraction activity whose goal is to discover hidden knowledge contained in large databases***
- Data Mining is used to find patterns and relationships in the data
- Data Mining is also called KDD
- KDD = Knowledge Discovery in Databases
- Data Mining is the killer app for scientific databases
- Examples:
**Clustering Analysis** = group together similar items and separate dissimilar items
**Classification Prediction** = predict the class label
**Regression** = predict a numeric attribute value
**Association Analysis** = detect attribute-value conditions that occur frequently together

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## Space Science Knowledge Discovery

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## Space Weather Example

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## Space Science Informatics

- Key enabler for new science discovery in large databases
- Large data science is here to stay
- Common data browse and discovery tools, and common data structures, will enable exponential knowledge discovery within exponentially growing data collections
- X-informatics represents the 3rd leg of scientific research:  experiment, theory, and data-driven exploration
- Space Science Informatics should parallel Bioinformatics and Geoinformatics:  become a stand-alone research sub-discipline

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## Future Work:  Informatics Applications

- Query-By-Example (QBE) science data systems:
- “Find more data entries similar to this one”
- “Find the data entry most dissimilar to this one”
- Automated Recommendation (Filtering) Systems:
- “Other users who examined these data also retrieved the following...”
- “Other data sets that are relevant to this data set include...”
- Information Retrieval Metrics for Scientific Databases:
- Precision:  “How much of the retrieved data is relevant to my query?”
- Recall:  “How much of the relevant data did my query retrieve?”
- Semantic Annotation (Tagging) Services:
- Report discoveries back to the science database for community reuse
- Science / Technical / Math (STEM) Education:
- Transparent reuse and analysis of scientific data in inquiry-based classroom learning (_[http://](http://serc.carleton.edu/usingdata/)__[serc.carleton.edu/usingdata](http://serc.carleton.edu/usingdata/)__[/](http://serc.carleton.edu/usingdata/)_ , _[DLESE.org](http://www.dlese.org/)_ )
- Key concepts that need defining (by community consensus): *Similarity, Relevance, Semantics (dictionaries, ontologies)*

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