1. e-Science: Stuart Anderson National e-Science Centre Stuart Anderson National e-Science Centre

2. Cool White Dwarves

3. Issues 1 Astronomers are looking for: –Many objects in globular clusters –Very faint objects –Interested in observations of many locations But: –The observations are noisy: Artifacts created by the sensor technology, scanning and digitizing. Junk in orbit, e.g. satellite tracks. Computer Science can help: -Pattern recognition, computational learning, data mining. -But: Astronomers are more picky. Astronomers are looking for: –Many objects in globular clusters –Very faint objects –Interested in observations of many locations But: –The observations are noisy: Artifacts created by the sensor technology, scanning and digitizing. Junk in orbit, e.g. satellite tracks. Computer Science can help: -Pattern recognition, computational learning, data mining. -But: Astronomers are more picky.

4. Cool Dwarves are faint and close The sky is full of faint objects. Cool White Dwarves are close. So they move about relative to the background stars. The illustrated observations cover a period of 30 years. We need to match up very faint objects observed by different equipment at different times.

5. Issues 2 Astronomers have a model of how luminous CWDs are that predicts how distant they are and hence how they move over time. We can use computational learning (aka data mining) to recognize CWDs provided we have a model that allows tractable learning. We can use the model to create training cases for various learning techniques. Astronomers also want to observe the same objects at different wavelengths. Models of objects can be used as a basis for data mining to link observations. Astronomers have a model of how luminous CWDs are that predicts how distant they are and hence how they move over time. We can use computational learning (aka data mining) to recognize CWDs provided we have a model that allows tractable learning. We can use the model to create training cases for various learning techniques. Astronomers also want to observe the same objects at different wavelengths. Models of objects can be used as a basis for data mining to link observations.

6. Problem Scale Cosmos (old technology), megabytes per plate. Super Cosmos (current technology), gigabytes per plate. Cosmos and Super Cosmos use 1m telescope images Vista (new technology): imaging in visible and x-ray using digital detectors, 4m telescope, terabytes per night. Sky surveys look at large-scale structure of space so many images are involved e.g. to estimate the density of CWDs in the galaxy. Cosmos (old technology), megabytes per plate. Super Cosmos (current technology), gigabytes per plate. Cosmos and Super Cosmos use 1m telescope images Vista (new technology): imaging in visible and x-ray using digital detectors, 4m telescope, terabytes per night. Sky surveys look at large-scale structure of space so many images are involved e.g. to estimate the density of CWDs in the galaxy.

7. E-Science and Old Science Computational models have been used for many years. e-Science systems will include vast collections of observed data. Scientific models are the essential organizing principle for data in such systems. Currently we are hand-crafting models that organise subsets of the data (e.g. CWDs). Can we create experimental environments that allow scientists to create new models of phenomena and test them against data? Computational models have been used for many years. e-Science systems will include vast collections of observed data. Scientific models are the essential organizing principle for data in such systems. Currently we are hand-crafting models that organise subsets of the data (e.g. CWDs). Can we create experimental environments that allow scientists to create new models of phenomena and test them against data?

8. Data, Information and Knowledge Much Grid work identifies a three-layer architecture for data. Data is the raw data acquired from sensors (e.g. telescopes, microscopes, particle detectors). Information is created when we “clean up” data to eliminate artifacts of the collection process. Knowledge is information embedded within an interpretive framework. Science provides strong interpretive frameworks Much Grid work identifies a three-layer architecture for data. Data is the raw data acquired from sensors (e.g. telescopes, microscopes, particle detectors). Information is created when we “clean up” data to eliminate artifacts of the collection process. Knowledge is information embedded within an interpretive framework. Science provides strong interpretive frameworks

9. Pattern: More science “in silico” Improved sensors, more sensors, huge increase in data volume. Need to “clean”, “mine” structure data. Support complex models and large-scale data collections inside the computer(s) Support for flexible model development and using models to organise and access data. E.g. in databases, spatial organisation, temporal organisation and support for queries exploiting that structure – useful for Geoscience? Improved sensors, more sensors, huge increase in data volume. Need to “clean”, “mine” structure data. Support complex models and large-scale data collections inside the computer(s) Support for flexible model development and using models to organise and access data. E.g. in databases, spatial organisation, temporal organisation and support for queries exploiting that structure – useful for Geoscience?

10. Credits Cosmos, Super Cosmos and Vista are projects looking at large scale structure of the cosmos, based at the Royal Observatory Edinburgh. Chris Williams, Bob Mann and Andy Lawrence are working on using computational learning to analyse super Cosmos data at RoE. Andy Lawrence is director of the AstroGrid project that is a major UK contribution to the international “Virtual Observatory” that will federate the worlds major astronomical data assets. Cosmos, Super Cosmos and Vista are projects looking at large scale structure of the cosmos, based at the Royal Observatory Edinburgh. Chris Williams, Bob Mann and Andy Lawrence are working on using computational learning to analyse super Cosmos data at RoE. Andy Lawrence is director of the AstroGrid project that is a major UK contribution to the international “Virtual Observatory” that will federate the worlds major astronomical data assets.

11. Whither Data Management? Scientific data is not particularly well behaved. In particular, it does not fit the relational model particularly well. We need new data models that are better suited to the needs of science (and everyone else too!). The model should attempt to support the work of scientists effectively. Current data models are not particularly useful. Scientific data is not particularly well behaved. In particular, it does not fit the relational model particularly well. We need new data models that are better suited to the needs of science (and everyone else too!). The model should attempt to support the work of scientists effectively. Current data models are not particularly useful.

12. Curated Databases Useful scientific databases are often curated : they are created/ maintained with a great deal of “manual” labour. select xyz from pqr where abc Database people’s idea of what happens What really happens DB1 DB2

13. Inter-dependence is Complex GERD TRRD GenBank Swissprot EpoDB TransFac GAIA BEAD A few of the 500 or so public curated molecular biology databases

14. Issues in Curated Databases Data integration (always a problem). Need to deal with schema evolution Data provenance. How do you track data back to its source (this information is typically lost) Data annotation. How should annotations spread through this network? Archiving. How do you keep all the archives when you are “publishing” a new database every day? Data integration (always a problem). Need to deal with schema evolution Data provenance. How do you track data back to its source (this information is typically lost) Data annotation. How should annotations spread through this network? Archiving. How do you keep all the archives when you are “publishing” a new database every day?

15. Archiving Some recent results on efficient archiving (Buneman, Khanna, Tajima, Tan) OMIM (On-line Mendelian Inheritance in Man) is a widely used genetic database. A new version is released daily. Bottom line, we can archive a year of versions of OMIM with <15% more space than the most recent version Some recent results on efficient archiving (Buneman, Khanna, Tajima, Tan) OMIM (On-line Mendelian Inheritance in Man) is a widely used genetic database. A new version is released daily. Bottom line, we can archive a year of versions of OMIM with <15% more space than the most recent version

16. A Sequence of Versions

17. “Pushing” time down [Driscoll, Sarnak, Sleator, Tarjan: “Making Data Structures Persistent.” ]

18. The final result (for the randomly selected data) Predicted expansion for a year’s archive: < 15%

19. Summary: technical issues Why and where: –better characterization of where (new ideas needed) –negation/aggregation Keys: –inference rules for relative keys –foreign key constraints –interaction between keys and DTDs/types Types for deterministic model (and other models). Annotation Temporal QLs and archives Why and where: –better characterization of where (new ideas needed) –negation/aggregation Keys: –inference rules for relative keys –foreign key constraints –interaction between keys and DTDs/types Types for deterministic model (and other models). Annotation Temporal QLs and archives

20. Pattern: Better support for work Data is increasingly complex and interdependent. “Curating” the data is continuous, and involves international effort to increase the scientific value of the data. Understanding the way we work with data is the key to providing adequate support for that work. Deeper support for projects working across the globe. Data is increasingly complex and interdependent. “Curating” the data is continuous, and involves international effort to increase the scientific value of the data. Understanding the way we work with data is the key to providing adequate support for that work. Deeper support for projects working across the globe.

21. Credits These issues are being addressed by Peter Buneman at Edinburgh. Peter has recently joined Informatics and NeSC. He has worked for a number of years on Digital Libraries and Biological Data Management. These issues are being addressed by Peter Buneman at Edinburgh. Peter has recently joined Informatics and NeSC. He has worked for a number of years on Digital Libraries and Biological Data Management.