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Virtual Observatory, Cyber-Science, and the Rebirth of Libraries S. G. Djorgovski (Caltech) With special thanks to Roy Williams, Alex Szalay,, Jim Gray, and many other VO founders and Cyber-Science pioneers …

An Overview:: 

An Overview: Astronomy in the era of information abundance The IT revolution, challenges and opportunities The Virtual Observatory concept What is it, how it all got started Virtual Observatory status Where are we now, where are we going From technology to science (and back) New tools for the science of 21st century Musings on cyber-science in general The changing nature of scientific inquiry The new roles of resarch libraries The changing nature of data and information needs

Astronomy is Facing a Major Data Avalanche : 

Astronomy is Facing a Major Data Avalanche Astronomy is Facing a Major Data Avalanche … … And so is every other science, and every other modern field of endeavor (commerce, security, etc.)


Astronomy is Now a Very Data-Rich Science Multi-Terabyte (soon: multi-PB) sky surveys and archives over a broad range of wavelengths … Billions of detected sources, hundreds of measured attributes per source … 1 nanoSky (HDF-S) 1 microSky (DPOSS)


Galactic Center Region (a tiny portion) 2MASS NIR Image


The exponential growth of data volume (and also complexity, quality) driven by the exponential growth in detector and computing technology … but our understanding of the universe increases much more slowly! Data  Knowledge ? Large digital sky surveys are becoming the dominant source of data in astronomy: ~ 10-100 TB/survey (soon PB), ~ 106 - 109 sources/survey, many wavelengths… Data sets many orders of magnitude larger, more complex, and more homogeneous than in the past doubling t ≈ 1.5 yrs


Panchromatic Views of the Universe: Data Fusion  A More Complete, Less Biased Picture Radio Far-Infrared Visible Visible + X-ray Dust Map Galaxy Density Map

Theoretical Simulations Are Also Becoming More Complex and Generate TB’s of DATA: 

Theoretical Simulations Are Also Becoming More Complex and Generate TB’s of DATA Structure formation in the Universe Supernova explosion instabilities


The Virtual Observatory Concept Astronomy community response to the scientific and technological challenges posed by massive data sets Harness the modern information technology in service of astronomy, and partner with it A complete, dynamical, distributed, open research environment for the new astronomy with massive and complex data sets Provide content (data, metadata) services, standards, and analysis/compute services Federate the existing and forthcoming large digital sky surveys and archives, facilitate data inclusion and distribution Develop and provide data exploration and discovery tools Technology-enabled, but science-driven


VO: Conceptual Architecture User Gateway


Surveys Observatories Missions Survey and Mission Archives Follow-Up Telescopes and Missions Data Services: Data discovery Warehousing Federation Standards … Compute Services: Data Mining and Analysis, Statistics, Visualization … Networking Digital libraries Primary Data Providers NVO Secondary Data Providers A Systemic View of the NVO Numerical Sim’s User Community International VO’s

Why is VO a Good Scientific Prospect?: 

Why is VO a Good Scientific Prospect? Technological revolutions as the drivers/enablers of the bursts of scientific growth Historical examples in astronomy: 1960’s: the advent of electronics and access to space Quasars, CMBR, x-ray astronomy, pulsars, GRBs, … 1980’s - 1990’s: computers, digital detectors (CCDs etc.) Galaxy formation and evolution, extrasolar planets, CMBR fluctuations, dark matter and energy, GRBs, … 2000’s and beyond: information technology The next golden age of discovery in astronomy? VO is the mechanism to effect this process

Information Technology  New Science: 

Information Technology  New Science The information volume grows exponentially Most data will never be seen by humans! The need for data storage, network, database-related technologies, standards, etc. Information complexity is also increasing greatly Most data (and data constructs) cannot be comprehended by humans directly! The need for data mining, KDD, data understanding technologies, hyperdimensional visualization, AI/Machine-assisted discovery … VO is the framework to effect this for astronomy

A Modern Scientific Discovery Process: 

A Modern Scientific Discovery Process Data Gathering Data Farming: Storage/Archiving Indexing, Searchability Data Fusion, Interoperability Data Mining (or Knowledge Discovery in Databases): Pattern or correlation search Clustering analysis, automated classification Outlier / anomaly searches Hyperdimensional visualization Data Understanding New Knowledge } Database Technologies Key Technical Challenges Key Methodological Challenges

How and Where are Discoveries Made?: 

How and Where are Discoveries Made? Conceptual Discoveries: e.g., Relativity, QM, Strings/Branes, Inflation … Theoretical, may be inspired by observations Phenomenological Discoveries: e.g., Dark Matter, Dark Energy, QSOs, GRBs, CMBR, Extrasolar Planets, Obscured Universe … Empirical, inspire theories, can be motivated by them New Technical Capabilities Observational Discoveries Theory IT/VO (VO) Phenomenological Discoveries:  Pushing along some parameter space axis VO useful  Making new connections (e.g., multi-) VO critical! Understanding of complex astrophysical phenomena requires complex, information-rich data (and simulations?)


Exploration of observable parameter spaces and searches for rare or new types of objects A simple, real-life example: Now consider ~ 109 data vectors in ~ 102 - 103 dimensions …

Exploration of the Time Domain …: 

Exploration of the Time Domain … An example (from DPOSS) of a new type of a phenomenon which may be discovered in a systematic exploration of the Time Domain: A normal, main-sequence star which underwent an outburst by a factor of > 300. There is some anecdotal evidence for such megaflares in normal stars. The cause, duration, and frequency of these outbursts is currently unknown. … and the advent of Synoptic Sky Surveys


An Example of a Synoptic Sky Survey: Palomar-Quest Palomar 40-inch telescope The 112-CCD camera Huge data rate: ~ 1 TB/month (but in < 10 yrs, we’ll have > 1 TB/day) Look for things that move… … and things that go Bang! in the night


Scientific Roles and Benefits of a VO Facilitate science with massive data sets (observations and theory/simulations) efficiency amplifier Provide an added value from federated data sets (e.g., multi-wavelength, multi-scale, multi-epoch …) Discover the knowledge which is present in the data, but can be uncovered only through data fusion Enable and stimulate some qualitatively new science with massive data sets (not just old-but-bigger) Optimize the use of expensive resources (e.g., space missions, large ground-based telescopes, computing …) Provide R&D drivers, application testbeds, and stimulus to the partnering disciplines (CS/IT, statistics …)

VO Developments and Status: 

VO Developments and Status The concept originated in 1990’s, developed and refined through several conferences and workshops Major blessing by the National Academy Report In the US: National Virtual Observatory (NVO) Concept developed by the NVO Science Definition Team (SDT). See the report at NSF/ITR funded project: A number of other, smaller projects under way Worldwide efforts: International V.O. Alliance A good synergy of astronomy and CS/IT Good progress on data management issues, a little on data mining/analysis, first science demos forthcoming

NVO Web Site: 

NVO Web Site


Registry Layer Existing Data Centers Data Services Semantics (UCD) SIAP, SSAP VOTable FITS, GIF,… OpenSkyQuery SkyQuery VOPlot OASIS conVOT Topcat Mirage Aladin DIS Disks, Tapes, CPUs, Fiber Grid Middleware SRB, Globus, OGSA SOAP, GridFTP data mining visualization image source detection Digital Library Other registries XML, DC, METS OAI ADS My Space storage services Databases, Persistency, Replication Virtual Data Workflow (pipelines) Discover Compute Publish Collaborate Authentication & Authorization crossmatch HTTP Services SOAP Services Grid Services stateless, registered & self-describing & persistent, authenticated Portals, User Interfaces, Tools Compute Services Bulk Access interfaces to data NVO Workflow Components

NVO: A Prototype Data Inventory Service: 

NVO: A Prototype Data Inventory Service DIS 1 2 3 4 Caltech NCSA JHU/StSci Goddard “What data are available for some object or some region on the sky? Can I get them easily?”

Data Inventory Service: 

Data Inventory Service

Data Inventory Service: 

Data Inventory Service

Data Inventory Service: 

Data Inventory Service

SkyQuery: NVO Prototype Catalog Cross-Matching Service: 

SkyQuery: NVO Prototype Catalog Cross-Matching Service … and much more is coming!


Broader and Societal Benefits of a VO Professional Empowerment: Scientists and students anywhere with an internet connection would be able to do a first-rate science A broadening of the talent pool in astronomy, democratization of the field Interdisciplinary Exchanges: The challenges facing the VO are common to most sciences and other fields of the modern human endeavor Intellectual cross-fertilization, feedback to IT/CS Education and Public Outreach: Unprecedented opportunities in terms of the content, broad geographical and societal range, at all levels Astronomy as a magnet for the CS/IT education “Weapons of Mass Instruction”

Slide29: (R. Williams et al.)


http:// A Coalition of the Willing?


Do We Know How to Run a VO? The VO is not yet another data center, archive, mission, or a traditional project It does not fit into any of the usual structures today It is inherently distributed, and web-centric It is fundamentally based on a rapidly developing technology (IT/CS) It transcends the traditional boundaries between different wavelength regimes, agency domains It has an unusually broad range of constituents and interfaces It is inherently multidisciplinary The VO represents a novel type of a scientific organization for the era of information abundance

Now Let’s Take A Look At Some Relevant Technology Trends …: 

Now Let’s Take A Look At Some Relevant Technology Trends …


Exponentially Declining Cost of Data Storage

Computing is Cheap …: 

Computing is Cheap … Today (~2004), 1 $ buys: 1 day of CPU time 4 GB (fast) RAM for a day 1 GB of network bandwidth 1 GB of disk storage for 3 years 10 M database accesses 10 TB of disk access (sequential) 10 TB of LAN bandwidth (bulk) 10 KWh = 4 days of computer time … Yet somehow computer companies make billions: you do want some toys, about $ 105 worth ≈ 1 postdoc year … But People are Expensive! People ~ Software, maintenance, expertise, creativity …

Moving Data is Slow!: 

Moving Data is Slow! Source: TeraScale Sneakernet, Microsoft Research, Jim Gray et al. How long does it take to move a Terabyte? (how about a Petabyte?)

Disks are Cheap and Efficient: 

Disks are Cheap and Efficient Price/performance of disks is improving faster than the computing (Moore’s law): a factor of ~ 100 over 10 years! Disks are now already cheaper than paper Network bandwith used to grow even faster, but no longer does And most telcos are bancrupt … Sneakernet is faster than any network Disks make data preservation easier as the storage technology evolves Can you still read your 10 (5?) year old tapes?

An Early Disk for Information Storage : 

An Early Disk for Information Storage Phaistos Disk: Minoan, 1700 BC No one can read it  (From Jim Gray)

The Gospel According to Jim Gray:: 

The Gospel According to Jim Gray: Store everything on disks, with a high redundancy (cheaper than the maintenance/recovery) Curate data where the expertise is Do not move data over the network: bring the computation to data! The Beowulf paradigm: Datawulf clusters, smart disks … The Grid paradigm (done right): move only the questions and answers, and the flow control You will learn to use databases! And we need a better fusion of databases and data mining and exploration

These Challenges Are Common!: 

These Challenges Are Common! Astronomical data volume ca. 2004: a few  102 TB (but PB’s are coming soon!) All recorded information in the world: a few  107 TB (but most of it is video, i.e., junk) The data volume everywhere is growing exponentially, with e-folding times ~ 1.5 yrs (Moore’s law) NB: the data rate is also growing exponentially! So, everybody needs efficient db techniques, DM (searches, trends & correlations, anomaly/outlier detection, clustering/classification, summarization, visualization, etc.) What others discover will help us, and maybe we can also help change the world (remember the WWW!)

The Evolution of Science: 

The Evolution of Science Empirical/Descriptive Analytical+Experimental A+E+Simulations A+E+S+DM/DE/KDD 1600 1700 1800 1900 1950 2000 t Computational science rises with the advent of computers Data-intensive science is a more recent phenomenon Technology The Evolving Role of Computing: Number crunching  Data intensive (data farming, data mining) A S E A A E E S DM Their interplay:

Some Musings on CyberScience: 

Some Musings on CyberScience Enables a broad spectrum of users/contributors From large teams to small teams to individuals Data volume ~ Team size Scientific returns ≠ f(team size) Human talent is distributed very broadly geographically Transition from data-poor to data-rich science Chaotic  Organized … However, some chaos (or the lack of excessive regulation) is good, as it correlates with the creative freedom (recall the WWW) Computer science as the “new mathematics” It plays the role in relation to other sciences which mathematics did in ~ 17th - 20th century (The frontiers of mathematics are now elsewhere…)

The Fundamental Roles of Research/University Libraries: 

The Fundamental Roles of Research/University Libraries To preserve, organize, and provide/facilitate access to scientific and scholarly data and results This purpose is constant, but the implementation and functionality evolve. What should the libraries become in the 21st century?

The Concept of Data (and Scientific Results) is Becoming More Complex: 

The Concept of Data (and Scientific Results) is Becoming More Complex Actual data (preserved) Data Virtual data (recomputed as needed) Primary Data And Metadata Derived Data Products And Results, Increasingly Distilled down Produced and often archived by the primary data providers Produced and published by the domain experts Information is cheap, but expertise and knowledge are expensive!

Scientific Publishing is Changing: 

Scientific Publishing is Changing Journals (and books?) are obsolete formats; must evolve to accommodate data-intensive science Massive data sets can be only published as electronic archives - and should be curated by domain experts Peer review / quality control for data and algorithms? The rise of un-refereed archives (e.g., very effective and useful, but highly heterogeneous and unselective A low-cost entry to publish on the web Who needs journals? Will there be science blogs? Persistency and integrity of data (and pointers) Interoperability and metadata standards

Research Libraries for the 21st Century: 

Research Libraries for the 21st Century How should research libraries evolve in the era of information abundance and complexity? What should be their roles / functionality? Data discovery services Data provider federators Primary and/or derived data archivers How much domain expertise should be provided? Quality control? Relationship with web portals and search engines? Is this too much for a single type of an institution? Are libraries obsolete (inadequate)? Should they split into several types of institutions? } Libraries As Virtual Organizations?

VO Summary: 

VO Summary National/International Virtual Observatory is an emerging framework to harness the power of IT for astronomy with massive and complex data sets Enable data archiving, fusion, exploration, discovery Cross the traditional boundaries (wavelength regimes, ground/space, theory/observation … Facilitate inclusion of major new data providers, surveys Broad professional empowerment via the WWW Great for E/PO at all educational levels It is inherently multidisciplinary: an excellent synergy with the applied CS/IT, statistics…and it can lead to new IT advances of a broad importa It is inherently distributed and web-based

But It Is More General Than That:: 

But It Is More General Than That: Coping with the data flood and extracting knowledge from massive/complex data sets is a universal problem facing all sciences today: Quantitative changes in data volumes + IT advances:  Qualitative changes in the way we do science (N)VO is an example of a new type of a scientific research environment / institution(?) in the era of information abundance This requires new types of scientific management and organization structures, a challenge in itself The real intellectual challenges are methodological: how do we formulate genuinely new types of scientific inquiries, enabled by this technological revolution?

… and the Evolution of Libraries: 

… and the Evolution of Libraries Scientific / research libraries must evolve, in order to stay useful in the era of data-intensive, computation-based science Database technologies are essential Fusion with data exploration technologies will be next A growing importance of domain expertise Blending in the web, then semantic web? For more details and links, please see

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