The Virtual Observatory

1. The Virtual Observatory Peter Fox HAO/ESSL/NCAR November 28, 2005

2. Outline • Virtual Observatories - history and definition(s), I*Ys • Some examples - within disciplines • When is a VO, not a VO? • Beyond disciplines, the emerging need • What is missing, i.e. the enabling technology • Challenges and interoperability • Examples: VSTO and SESDI • What’s ahead

3. Encyclopedia - we’ve made it! • Virtual observatory is a collection of integrated astronomical data archives and software tools that utilize computer networks to create an environment in which research can be conducted. Several countries have initiated national virtual observatory programs that will combine existing databases from ground-based and orbiting observatories and make them easily accessible to researchers. As a result, data from all the world's major observatories will be available to all users and to the public. This is significant not only because of the immense volume of astronomical data but also because the data on stars and galaxies has been compiled from observations in a variety of wavelengths: optical, radio, infrared, gamma ray, X-ray and more. Each wavelength can provide different information about a celestial event or object, but also requires a special expertise to interpret. In a virtual observatory environment, all of this data is integrated so that it can be synthesized and used in a given study. • http://www.encyclopedia.com/html/v1/virtobserv.asp

4. Yet more definitions • AVO: A virtual observatory (VO) is a collection of interoperating data archives and software tools which utilize the internet to form a scientific research environment in which astronomical research programs can be conducted. In much the same way as a real observatory consists of telescopes, each with a collection of unique astronomical instruments, the VO consists of a collection of data centres each with unique collections of astronomical data, software systems and processing capabilities. • From the Grid: virtual observatory - astronomical / solar / solar terrestrial data repositories made accessible through grid and web services. • Workshop: A Virtual Observatory (VO) is a suite of software applications on a set of computers that allows users to uniformly find, access, and use resources (data, software, document, and image products and services using these) from a collection of distributed product repositories and service providers. A VO is a service that unites services and/or multiple repositories. • VxOs

5. Virtual Observatories • Conceptual examples: • In-situ: Virtual measurements • Related measurements • Remote sensing: Virtual, integrative measurements • Data integration • Systems or frameworks? • Brokers, or data providers, or service providers? • VOTables, VOQueries, etc. a sytnax for exchange • Holding metadata? Who imposes the catalog, or vocabulary?

6. VOs and data providers • Not a VO: • When you hand off a user to another site • Only one dataset • When you do not deliver, or do not arrange for delivery of the data • When your curation role is not evident • DP: • Acquire data and produce data products (static or dynamic). • Preserve data in useable forms. • Distribute data, and provide easy machine (API) and Internet browser access. • Support a communication mechanism – should support a standards-based messaging system (e.g., ftp, http, SOAP, XML) • Produce, document, and make easily available metadata for product finding and detailed data granule content description. Ideally, maintain a catalogue of detailed data availability information. • Assure the validity and quality of the data. • Document the validation process. • Provide quality information (flags). • Maintain careful versioning including the processing history of a product. • Maintain an awareness of standards (such as community accepted data models), and adhere to them as needed. • Provide software required to read and interpret the data; ideally the routines used by the PI science team should be available to all.

7. What should a VO do? • Make “standard” scientific research much more efficient. • Even the PI teams should want to use them. • Must improve on existing services (Mission and PI sites, etc.). VOs will not replace these, but will use them in new ways. • Enable new, global problems to be solved. • Rapidly gain integrated views from the solar origin to the terrestrial effects of an event. • Find data related to any particular observation. • (Ultimately) answer “higher-order” queries such as “Show me the data from cases where a large CME observed by SOHO was also observed in situ.”

8. What the NASA community wants • Provide coordinated discovery and access to data and service resources for a specific scientific discipline • Identify relevant data sources and appropriate repositories. • Allow queries that yield data granules or pointers to them. • Provide a user interface to access resources both through an API (or equivalent machine access) and a web browser application. • Handle a wide range of provider types, as needed. • Understand the data needs of its focus area: • Recruit potential new providers. • Provide support and "cookbooks" for easy incorporation of providers. • Help to assure high data quality and completeness of the product set. • Resolve issues of multiple versions of datasets.

9. More from NASA • Provide documentation for metadata: • Set standards for metadata and query items • Assist providers, and review metadata. • Maintain a global knowledge of data availability. • Possibly maintain collection catalog metadata. • Provide an API or other means for the VxO to appear to others as a single provider. • Potentially provide value-added services (can be done by providers or elsewhere): • Data Subsetting: • Averaging of data • Filtering • Data Merging • Format Conversion • Provide access to event lists and ancillary data. • Collect statistical information and community comments to assess success.

10. VSO and the ‘small box’

16. CEDAR

21. The Earth System Grid DATA storage SECURITY services METADATA services TRANSPORT services LBNL ANALYSIS & VIZ services MONITORING services gridFTP server/client HRM FRAMEWORK services DISK ANL Auth metadata NCAR MySQL GSI CAS server RLS SLAMON daemon TOMCAT AXIS GRAM CAS client GSI NCL openDAPg client LAS server NERSC HPSS gridFTP server/client HRM openDAPg server ORNL NCAR MSS DISK TOMCAT LLNL SLAMON daemon CDAT openDAPg client MySQL Xindice RLS THREDDS catalogs gridFTP server/client HRM gridFTP server/client HRM CAS client MyProxy client MyProxy server GSI ORNL HPSS DISK DISK openDAPg server ISI MySQL MySQL RLS MySQL Xindice RLS MCS OGSA-DAIS CAS client GSI GSI GSI

25. Emerging needs • Interdisciplinary science and engineering (not just between adjacent fields) • Interdisciplinary data assimilation, integration • Web service workflow orchestration (beyond syntax) • Vortals as well as portals (specific to general) • Agency (NASA) and community efforts (eGY, IHY, IPY, IYPE)

26. ACOS at the MLSO Near real-time data from Hawaii from a variety of solar instruments, as a valuable source for space weather, solar variability and basic solar physics

27. CISM Goal: To create a physics-based numerical simulation model that describes the space environment from the Sun to the Earth. THE USES OF SPACE WEATHER MODELING A scientific tool for increased understanding of the complex space environment. A specification and forecast tool for space weather prediction. An educational tool for teaching about the space environment.

28. CEDARWEB Community data archive, documents, and support.

29. User requirements • CEDAR • Search must return data (i.e. no null searches) • Search across instruments, models • Know about special time periods, campaigns, etc. • Allow selections based on (appropriate) geophysical conditions, e.g. Kp index • Usual format returned and in correct units • Must be able to easily re-create the search, access • Visual browsing • MLSO • Same as CEDAR !! • + sampling interval choice, e.g. minutely, daily, average, best of the day, synoptic

30. Challenges and interoperability • Semantic misunderstanding • E.g. sunspot number and variations in solar radiation: over 90% of researchers outside the sub-field of solar radiation think: sunspot number is a measure of solar radiation • In reality: a sunspot number is a measure of the number of sunspots appearing on the visible solar surface, a sunspot is an indicator of the location of strong solar magnetic fields, strong magnetic fields are collectively known as solar activity, sunspots are observed to produce a localized decrease in the solar radiation output, etc. • How to ‘explain’ this to a computer? • Interfaces are built by computer scientists with syntax that often works within a discipline but rarely across them

31. Concept and user needs Goal - find the right balance of data/model holdings, portals and client software that a researchers can use without effort or interference as if all the materials were available on his/her local computer. The Virtual Solar-Terrestrial Observatory (VSTO) is a: • distributed, scalable education and research environment for searching, integrating, and analyzing observational, experimental and model databases in the fields of solar, solar-terrestrial and space physics VSTO comprises a: • System-like framework which provides virtual access to specific data, model, tool and material archives containing items from a variety of space- and ground-based instruments and experiments, as well as individual and community modeling and software efforts bridging research and educational use

32. User needs In discussions with data providers and users, the needs are clear: ``Fast access to `portable' data, in a way that works with the tools we have; information must be easy to access, retrieve and work with.'’ • Few clicks, get what I want, whose tools? MY tools Too often users (and data providers) have to deal with the organizational structure of the data sets which varies significantly --- data may be stored at one site in a small number of large files while similar data may be stored at another site in a large number of relatively smaller files. There is an equally large problem with the range of metadata descriptions for the data. Users often only want subsets of the data and struggle with getting it efficiently. One user expresses it as: ``(Please) solve the interface problem.'’ • Encapsulate more

33. What’s new in the VSTO? • Datasets alone are not sufficient to build a virtual observatory: VSTO integrates tools, models, and data • VSTO addresses the interface problem, effectively and scalably • VSTO addresses the interdisciplinary metadata and ontology problem - bridging terminology and use of data across disciplines • VSTO leverages the development of schema that adequately describe the syntax (name of a variable, its type, dimensions, etc. or the procedure name and argument list, etc.), semantics (what the variable physically is, its units, etc.) and pragmatics (or what the procedure does and returns, etc.) of the datasets and tools. • VSTO provides a basis for a framework for building and distributing advanced data assimilation tools

34. Virtual Observatory: Need better glue • Basic problem: schema are categorized rather than developed from an object model/class hierarchy -> significantly limits non-human use. However, they all form the basis to organize catalog interfaces for all types of data, images, etc. • This limits data systems utilizing frameworks and prevents frameworks from truly interoperating (SOAP, WSDL only a start) • Directories, e.g. NASA GCMD, CEDAR catalog, FITS (flat) keyword/ value pairs, are being turned into ontologies (SWEET, VSTO) • Markup languages, e.g. ESML, SPDML, ESG/ncML are excellent bases

35. Methodologies • Use-cases • User requirements • Semantics - ‘what does this mean’ • Data integration • Ontologies • Rapid prototyping

36. HAO and SCD from NCAR, McGuinness Assoicates: Peter Fox, Don Middleton, Stan Solomon, Deborah McGuinness, Jose Garcia, Patrick West, Luca Cinquini, James Benedict, Tony Darnellhttp://vsto.hao.ucar.edu/ and soon http://www.vsto.org/ • Application domains - CEDAR, CISM, ACOS • Realms (ontologies): • Covers middle atmosphere to the Sun + SPDML • Mesh with Earth Realm (SWEET) • Mesh with GEON • Use-cases and user requirements VSTO SWEET +SPDML ACOS CISM CEDAR

37. VSTO Use-case 1 UC1: Plot the observed/measured Neutral Temperature (Parameter) looking in the vertical direction for Millstone Hill Fabry-Perot interferometer (Instrument) from January 2000 to August 2000 (Temporal Domain) as a time series . Precondition: portal application is authorized to access the backend data extraction and plotting service • User accesses the portal application • User goes through a series of views to select (in order) the desired observatory, instrument, record-type (kind of data), parameter, start and stop dates, and the plot type is inferred. At each step, the user selection determines the range of available options in the subsequent steps. NB, an alternate path is selection of start and stop dates, then instrument, etc. 3. The application validates the user request: verifying the logical correctness of the request, i.e. that Millstone Hill is an observatory that operates a type of instrument that measures neutral temperature (i.e. check that Millstone Hill <isA> observatory and check that the range of the measures property on the Millstone Hill Fabry Perot Interferometer subsumes neutral temperature). Also, the application must verify that no necessary information is missing from the request. 4. The application processes the user request to locate the physical storage of the data, returning for example a URL-like expression: find Millstone Hill FPI data of the correct type (operating mode; defined by CEDAR KINDAT since the instrument has two operating modes) in the given time range (Millstone Hill FPI <hasKindofData> 1701 <intersects> TemporalDomain [January 2000, August 2000] ) 5. The application plots the data in the specified plot type (a time series). This step involves extracting the data from records of one or more files, creating an aggregate array of data with independent variable time (of day or day+time depending on time range selected) and passing this to a procedure to create the resulting image.

42. Demo

43. Have you heard these questions? • What do you mean by that? • What did you mean by that? • What does this mean? • How did you get this, please explain? • Does this also mean … ? • Doesn’t this contradict … ? • <Insert your own here> • Leads to: • Inference • Reasoning • Explanation

44. Paradigm shift for NASA? • From: Instrument based • To: Measurement based • Requires: ‘bridging the discipline data divide’ • Overall vision for SESDI: To integrate information technology in support of advancing measurement-based processing systems for NASA by integrating existing diverse science discipline and mission-specific data sources. SWEET Volcano Climate SESDI

45. Semantic connectors • The SESDI re-useable component interfaces. The stub on each end of the connector is based on the GEON Ontology-Data registration technology and contains articulated axioms derived from the knowledge gained in the unit-level data registration. Includes integrity checks, domain and range, etc. SWEET Process-oriented semantic content represented in SWSL ---------------------------- Articulation axioms

46. What’s ahead? • Virtual Observatories provide both framework and data system elements, users are already confusing VOs and data providers • Many VO’s are noting the need for better glue, scalability, expandability, etc. • Success (to date) in utilizing formal methods for interface specification and development using ontologies • Success in breaking all of the free tools! Commercial tools are under consideration • Challenges exist for reasoning and interface with scientific datatypes, e.g. complex spatial and temporal concepts • For VSTO (and SESDI) - more use-cases, populate the interfaces and test for scalability and interoperation in production settings