The GRID Adventures: SDSC's Storage Resource Broker and Web Services in Digital Library Applications

1
The GRID Adventures SDSC's Storage Resource
Broker and Web Services in Digital Library
Applications

• Arcot Rajasekar, Reagan Moore, Bertram Ludäscher,
  Ilya Zaslavsky
• ZASLAVSK_at_SDSC.EDU
• San Diego Supercomputer Center
• University of California, San Diego

2
Data and Knowledge Systems

• Staff
• Reagan Moore
• Chaitan Baru

• Data Mining Lab (Tony Fountain)
• Advanced Query Processing Lab (Amarnath Gupta)
• Knowledge-Based Integration Lab (Bertram
  Ludäscher)
• Data Grid Lab (Arcot Rajasekar)
• Spatial Information Systems Lab (Ilya Zaslavsky)
• 2-3 programmers in each lab, graduate and
  undergraduate students
• Now connecting research with production
  databases and data grid solutions

3
Overview

• Intro
• SDSC and NPACI
• Part I technologies
• What is Data Grid
• Data, Information, and Knowledge Infrastructures
  at SDSC/DICE
• SDSC Storage Resource Broker, with examples
• MIX (Mediation of Information Using XML), and
  Knowledge-Based Mediation
• Part II case studies
• BIRN the First Operational Data Grid
• Web Services Demos
• Persistent Archives at SDSC
• Summary

4
A Distributed National Laboratory for
Computational Science and Engineering
5
1st Teraflops System for US Academia
Nov 1999

• 1 TFLOPs IBM SP
• 144 8-processor compute nodes
• 12 2-processor service nodes
• 1,176 Power3 processors at 222 MHz
• Initially gt 640 GB memory (4 GB/node), upgrade to
  gt 1 TB later
• 6.8 TB switch-attached disk storage
• Largest SP with 8-way nodes
• High-performance access to HPSS

6
Bioinformatics Infrastructure for Large-Scale
Analyses

• Next-generation tools for accessing,
  manipulating, and analyzing biological data
• Biology, Stanford University
• DICE, SDSC
• Analysis of Protein Data Bank, GenBank and other
  databases
• Accelerate key discoveries for health and
  medicine
• Supporting and leveraging new data grid projects,
  such as BIRN in biology

7
Part I technologies
SRB

• What is Data Grid
• Data, Information, and Knowledge Infrastructures
  at SDSC/DICE
• SDSC Storage Resource Broker
• MIX (Mediation of Information Using XML), and
  Knowledge-Based Mediation

8
What are Data Grids?

• Power Grid Analogy
• Multiple power generators
• Complex transmission networks with switching
• Simple Usage Interface plug and play
• Guaranteed Supply - Meeting of demands (peak and
  lull)
• Complex cost function
• More than one data provider
• Best movement of data across computer networks
• Seamless Access to Data with good Finding Aids
  
• Guarantee of Data Access
• Access Control, Quotas Complex Usage Costing

9
Data Grids
Data Grid - linking multiple data
collections Separate name spaces Separate
schema Separate administration
domains Heterogeneous database instances
Database A
Database B
Data grid
The data grid is itself a collection that
provides mechanisms to hide latency and manage
semantics
10
Federated Digital Libraries
Virtual Data Grid - linking multiple data
collections Ability to execute processes to
recreate derived data
Database A Services
Database B Services
Virtual Data Grid
The virtual data grid integrates data grid and
digital library technology to manage processes
11
Why Data Grids Data Handling Problems

• Large Datasets Large Number of Datasets Scaling
• Distributed, Heterogeneous Storage
• Virtualization Transparency
• Collaboration, Access Control, Authentication,
  Security
• Replication, Coherency, Synchronization
• Fault Tolerance and Load Distribution
• Scheduling, Caching Data Placements
• Data Migration over Time Space
• Data/Collection Curation
• Uniform Name Space
• Handling Legacy Data and Data/Resource Evolution
• User-friendly Interfaces foster collaborations

12
Why Data Grids Metadata Problems

• Types of Metadata Relational to XML to
  unstructured
• Standardized to User-defined Metadata
• Large Number of Attributes
• Large Size Scaling
• Federation - integration over space
• Evolution - integration over time
• Evolution - integration over contexts
• Discovery and Search
• Presentation user friendly
• Extraction and Maintenance

13
DAKS Data Management Hierarchy

• Model-Based Information Management
• Rule-based ontology mapping, conceptual-level
  mediation - CMIX
• Information Mediation
• Data federation across multiple libraries - MIX
• Digital Library
• Interoperable services for information discovery
  and presentation - SDLIP
• Data Collection
• Tools for managing data set collections on
  databases - MCAT
• Data Handling
• Systems for data retrieval from remote storage -
  SRB
• Persistent Archives
• Storage of data collections for 30 years

14
SRB as a Solution

• The Storage Resource Broker is a middleware
• It virtualizes resource access
• It mediates access to distributed heterogeneous
  resources
• It uses a MetaCATalog to facilitate the
  brokering
• It integrates data and metadata

MCAT
Application
SRB Server
HRM DB2, Oracle, Illustra, ObjectStore
HPSS, ADSM, UniTree
UNIX, NTFS, HTTP, FTP
15
SRB Architecture
Data/Metadata Resources
MCAT
All Processing, Storage, Metadata and
Visualization resources are logical and are
mapped to physical resources by SRB
16
Solution SRB
SDSC Storage Resource Broker Meta-data
Catalog
17
SRB Space
SRB
SRB
SRB
SRB
SRB
SRB
SRB
DL
DR - Data Repository DL - Dig Library MC - Meta
Catalog
SRB
SRB
SRB
18
MySRB Web-bases Access to the SRB

• Browse in Hierarchical Collections
• Registration of
• (remote) Legacy Files Directories
• Registration of SQL Objects
• Registration of URLs
• Data Movement Operations
• Ingest Re-Ingest, Delete, Unlink
• Replicate, Copy, Move, S-Link
• Access Control Operations
• Read, Write, Own, Curate, Annotate,
• Ticket-based Access
• Version Control Operations
• Read Lock, Write Lock, Unlock
• Check In Check Out

19
Meta data Management in MySRB

• Types of Meta Data
• System-level Metadata
• Size, resource, owner, date, access control,
• User-defined Meta data
• for data collections
• ltname,value,unitgt triples
• No limits in number of metadata
• Support for Collection-level schemas
• Comments, default values, drop-down lists
• Support for Standardized Schemas
• (eg. Dublin Core)
• Annotations
• Supports textual annotations
• Annotator, date, context also registered

20
SRB Projects

• Digital Libraries
• UCB, Umich, UCSB, Stanford,CDL
• NSF NSDL - UCAR / DLESE
• NASA Information Power Grid
• DOE ASCI Data Visualization Corridor
• Astronomy
• National Virtual Observatory
• 2MASS Project (2 Micron All Sky Survey)
• Particle Physics
• Particle Physics Data Grid (DOE)
• GriPhyN
• SLAC Synchrotron Data Repository
• Medicine
• Visible Embryo (NLM)
• Earth Systems Sciences
• ESIPS
• LTER
• Persistent Archives
• NARA

21
Large Data Project Examples

• Astronomy
• National Virtual Observatory
• Integrate 18 sky surveys- (ITR prop)
• 2MASS Project (2 Micron All Sky Survey)
• 10TB 5million files
• Co-locate Images for Spatial Access
• Data Mining across entire collection
• Replicate to CalTech HPSS
• Particle Physics
• Particle Physics Data Grid (DOE)
• GrPhyN (NSF ITR proj)
• CERN LHC 1PB/yr (1billion obj)
• Multi-Lab integration
• SLAC Synchrotron Data
  Repository

22
National Virtual Observatory Data Grid
1. Portals and Workbenches
2.Knowledge Resource Management
Bulk Data Analysis
Metadata View
Data View
Catalog Analysis
3.
Standard APIs and Protocols
Concept space
4.Grid Security Caching Replication Backup Schedul
ing
Information Discovery
Metadata delivery
Data Discovery
Data Delivery
5.
Standard Metadata format, Data model, Wire format
Catalog Mediator
6.
Data mediator
Catalog/Image Specific Access
Compute Resources
Catalogs
Data Archives
Derived Collections
7.
23
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24
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25
Digital Sky Data Ingestion
Data Cache
SRB SUN E10K
star catalog
Informix
SUN
HPSS
800 GB
.
input tapes from telescopes
10 TB
SDSC
IPAC CALTECH
26
Digital Sky Data Ingestion

• The input data was on tapes in a random
  (temporal) order.
• Ingestion nearly 1.5 year - almost continuous, 4
  parallel streams (4 MB/sec per stream), 247365
• Total 10TB, 5 million, 2 MB images in 147,000
  containers.
• SRB performed a spatial sort on data insertion
  (Scientists view/analyze data by neighborhood).
  The disc cache (800 GB) for the HPSS containers
  was utilized.
• Ingestion speed limited by input tape reads
• Only two tapes per day can be read
• Work flow incorporated persistent features to
  deal with network outages and other failures.
• C API was utilized for fine grain control and to
  be able to manipulate and insert metadata into
  Informix catalog at IPAC Caltech.
• http//www.ipac.caltech.edu/2mass

27
DigSky Conclusion

• SRB can handle large number of files
• Metadata access is still less than ½ sec delay
• Replication of large collections
• Single command for geographical replication
• On-the-fly sorting (out-of-tape sorting)
• Availability of data otherwise not possible
• Near-line access to 5 million files (10 TB)
• Successfully used in web-access large scale
  analysis (daily)

28
Demonstration

• goto mySRB
• For Additional Information
• http//www.npaci.edu/dice/srb
• srb_at_sdsc.edu

29
MIXMediation of Information using XML
30
Mediation of Information using XML (MIX)
XML Query
XML

• Export
• Schema Metadata
• (DTD, RDF,)
• Capabilities

XML View Document(s)
XML View Document(s)
XML View Document(s)
Wrapper
Wrapper
Data Source (eg. home ads)
Native XML Database
Legacy Source
31
A Typical Mediation Scenario
User Interface
Query
Results
Mediator (integrated views over heterogeneous
sources)
Query fragment
Query fragment
Convert incoming query and outgoing data
Wrapper
Wrapper
Wrapper
SQL Database
GIS
HTML
32
The Home Buyer Scenario
Web Client
XMAS Query
Results (XML)
MIXm Mediator
Homes mediator
Data
Data
Neighborhood mediator
National test scores
Data
Schools mediator
Home info (real estate)
Community info (name, ZIP)
Crime info (ZIP, stats)
Nhood info (demographics)
Schools info (address, size)
School district info (scores,spending,ZIP)
www.realtor.com
www.sandag.cog.ca.us
www.sannet.gov
www.homeadvisor.msn.com
www.asd.com
33
Home Buyer GUI
34
An XML Query (XMAS)
Clt.condogt ltaddress zipZ/gt lt/condogt
AT www.condo.com AND Slt.school
typeelementarygt ltaddress zipZ/gt
lt/schoolgt AT schools.org
ltfoldergt C S for S lt/foldergt for C
ltcondosAndSchoolsgt ltfoldergt ltcondogt
ltaddress ... zip92037gt ltpricegt170k
OBOlt/pricegt ltbedroomsgt2lt/bedroomsgt
lt/condogt ltschoolgt ltnamegtLa Jolla
Highlt/namegt ltaddress zip92037gt
lt/schoolgt ltschoolgtlt/schoolgt lt/foldergt

... ltRealEstateAgentgt ltnamegtJ. Smithlt/namegt
ltcondosgt ltcondogt ltaddress ...
zip92037gt ltpricegt170k OBOlt/pricegt
ltbedroomsgt2lt/bedroomsgt lt/condogt ltcondosgt
lt/RealEstateAgentgt
35
Home Buyer GUI (Answers)
Generated XMAS Query
XML Answer Document
36
Our Research

• In what query language does the user pose a
  query?
• How does the query engine of the mediator rewrite
  the query?
• How does the mediator combine/restructure/post-pro
  cess partial results?
• What data model and query transformation scheme
  should the wrappers use for different source
  types?
• For details http//www.npaci.edu/DICE/MIX

XMAS
XML
37
New MIX Challenges from Scientific Applications

• Complex Data
• SDSCs Scientific Data Applications
  (current/planned, e.g. Neurosciences NCMIR, NIH
  BIRN, Earth sciences GEON, GeoGrid, ...) show
  that syntactic/structural integration is
  insufficient for ...
• Complex Multiple-World Mediation Problems
• complex, disjoint, seemingly unrelated data
• hidden semantics in complex, indirect
  relationships
• gt Semantic (aka Model/Knowledge-Based) Mediation
  
• lift mediation to the level of conceptual models
  (CMs)
• use domain experts knowledge formalized as rules
  over CMs
• gt Specialized Extensions
• temporal, geospatial, statistical, DQ/accuracy...
  operations
• gt Extend Mediation Scope and Power via Deductive
  Rules

38
INFORMATION MEDIATION WITH DOMAIN MAPS
39
An Unresolved ChallengeHow do nerve cells
change as we learn and remember?
A multi-resolution study of the rat hippocampus
at Boston University
40
Dendritic spine morphology and its variations
density spines/length
Reconstructions from the Synapse Lab, Boston
University
41
Observations

• Spine density, size, shape and PSD vary with
  maturity
• Spine neck geometry controls peak Calcium amount
• Calcium flow parameters depend on the different
  subclasses of spines

42
Example for Formalizing Domain KnowledgeDomain
Map for SYNAPSE and NCMIR

• A domain map comprises
• Description Logic facts ...
• - concepts ("classes")
• - roles ("associations")
• derived properties ...
• ... expressed as logic rules
• - (e.g. F-logic)

43
Extended Mediator Architecture for Semantic
Mediation
USER/Client
CM (Integrated View)
Domain Map DM
Integrated View Definition IVD
CM Plug-In
CM Queries Results (exchanged in XML)
Logic API (capabilities)
44
Comparison Summary Semantic Mediation
45
Part II case studies

• BIRN
• Web Services
• Persistent Archives

46
NIH is Funding a Brain Imaging Federated
Repository
Biomedical Informatics Research Network (BIRN)
NIH Plans to Expand to Other Organs and Many
Laboratories
Part of the UCSD CRBS Center for Research on
Biological Structure
National Partnership for Advanced Computational
Infrastructure
47
Infrastructure for Sharing Neuroscience Data

• SOURCES
• NCMIR, U.C. San Diego
• Caltech Neuroimaging
• Center for Imaging Science, John Hopkins
• Center for Computational Biology, Montana State
• Laboratory of Neuro Imaging (LONI), UCLA
• Computatuonal Neurobiology Laboratory, Salk
  Inst.
• Van Essen Laboratory, Washington University
• Data Management Infrastructure (DAKS/NPACI)
• MIX Mediation in XML
• MCAT information discovery
• SRB data handling
• HPSS storage
• ...

Knowledge-based GRID infrastructure
?
?
?
?
Data Management Infrastructure (Data
Grid) GTOMO, Telemicroscopy, Globus, SRB/MCAT,
HPSS
48
Data Sharing Scenario
Duke
UCLA
NCMIR
MCAT
SRB Virtual Data Grid (BIRN)
CalTech
SDSC
Biomedical Imaging Resource Network
49
The Need for Semantic Integration
Cross-source queries
What is the cerebellar distribution of rat
proteins with more than 70 homology with human
NCS-1? Any structure specificity? How about other
rodents?
Cross-source relationships are modeled
Semantic (knowledge-based) mediation services
Data, relationships, constraints are modeled (CMs)
Wrapper
Wrapper
Wrapper
Wrapper
Web
protein localization
morphometry
neurotransmission
CaBP, Expasy
50
Hidden Semantics Protein Localization

• ltprotein_localizationgt
• ltneuron typepurkinje cell /gt
• ltprotein channelredgt
• ltnamegtRyRlt/gt
• .
• lt/proteingt
• ltregion h_grid_pos1 v_grid_posAgt
• ltdensitygt
• ltstructure fraction0.8gt
• ltnamegtspinelt/gt
• ltamount nameRyRgt0lt/gt
• lt/gt
• ltstructure fraction0.2gt
• ltnamegtbranchletlt/gt
• ltamount nameRyRgt30lt/gt
• lt/gt

51
Mediation Services Source Registration (System
Issues)
Source
Data Type
Query Capability
Result Delivery
Access Protocol
ARC
SQL
XML QL
DOOD
table
tree
file
SRB
HTTP
JDBC
Tuple-at-a-time
Stream
Set-at-a-time
SPJ
Selections
Binary for Viewer
52
Mediation Services Source Registration
(Semantics Issues)

• Domain Map Registration
• provide concept space/ontology
• as a private object (myANATOM)
• merge with others (give semantic bridges)
• and check for conflicts
• Conceptual Model Registration
• schema classes, associations, attributes
• domain constraints
• put data into context (linking data to the
  domain map)

Next
53
Mediation Services Integrated View Definition

• DERIVE
• protein_distribution(Protein, Organism,
  Brain_region, Feature_name, Anatom, Value)
• FROM
• Iprotein_label_image proteins -gtgt Protein
  organism -gt Organism anatomical_structures -gtgt
• ASanatomical_structurename-gtAnatom ,
  from PROLAB
• NAEneuro_anatomic_entityname-gtAnatom
  from ANATOM
• located_in-gtgtBrain_region,
• AS..segments..featuresname-gtFeature_name
  value-gtValue.

• provided by the domain expert and mediation
  engineer
• declarative language (here Frame-logic)

54
Mediation Services Semantic Annotation Tools
line drawing ? annotation ? (spatial) database
for mediation
55
Part II case studies

• Web Services

56
Web Services Demo 1
Find school districts in San Diego where computer
ownership rates among residents are over 80
Clients AxioMap, Polexis
Java Servlet
XML Mediator (Enosys)
Spatial Mediator
XML query (XCQL)
XML
WSDL
WSDL
Web Server SOAP
Web Server SOAP
Sociology Workbench
Boundaries of municipalities and school districts
San Diego Digital Divide Survey
Java Servlets
Java Servlets
Oracle DBMS
Oracle DBMS
57
Web Services Demo 2
Web spatial source, EPA data ArcObjects spatial
service
Spatial Mediator
Java Servlet
XML
WSDL
Web Server SOAP
ESRI ArcObjects
XML Wrapper
XML Wrapper
EPA Envirofacts Website
Local Pollution Data
Coordinate Conversion Service
58
Web Services Demo 3
GIS source, WSDL for spatial analysis,
survey data analysis, DBMS query UCR/FBI
data Process flow across Web services
Counties crossed by an interstate
Counties with decrease in homicide rates over
, 1993-99
Spatial Query, ArcIMS/ArcObjects
Counties with decrease in victims of firearms
over , 1993-99
WSDL
WSDL
UCR summaries, Oracle
Victim data, SWB
59
Part II case studies

• Persistent Archives

60
Persistent Archives

• NARA project
• Store Recover Data after 400 years
• 5 million emails
• 33 million web pages
• 90 million personnel records

61
Persistent Archives

• Challenges each of the software and hardware
  systems may become obsolete
• the storage media may degrade
• the storage system may become obsolete
• the database backups may become obsolete, with no
  way to recover the collection (structure)
• the digital object formats may become obsolete,
  with no helper application that can read them
• Persistent archive is a migration mechanism
• support for automatic migration to new
  technology automatic ingestion, management,
  access, catalog discovery
• Infrastructure independence
• Non-proprietary formatting -- Collection
  management -- Data set access Authentication --
  Presentation
• Persistent archive is an interoperability system
• XML as a (meta-) information markup language

62
Persistent Archive
Persistent archive Describe archived data as
collections Describe processes used to create
collections Manage evolution of technology
Database A (today)
Database A (tomorrow)
Virtual Data Grid
The persistent archive is itself a virtual data
grid that provides mechanisms to manage
migration to new technology
63
Information Hierarchy (Simplest Definitions)

• Data
• digital object, i.e., the object representation
  as a bit stream
• Information
• any tagged data, where tags are treated as
  information attributes
• attributes may be tagged data within the digital
  object, or tagged data that is associated with
  the digital object
• Knowledge
• higher-order concepts and relationships between
  attributes
• relationships can be procedural, temporal,
  structural, spatial, functional, ... and
  described in a Logic formalism (semantic
  networks, description logics, conceptual graphs,
  ...) which is often rule-based (e.g. Datalog,
  Frame-Logic)

64
What Types of Interoperability are Needed?

• Data management (digital objects)
• ability to work with multiple types of storage
  systems, across separate administration domains
• Information management (attributes)
• ability to define a collection independent of
  database choice
• ability to migrate collection onto new databases
• Knowledge management (relationships)
• ability to manage relationships and high-level
  domain concepts
• ability to map concepts to collection attributes

65
From XML-Based to Knowledge-Based Archives

• Collection-based archival with XML save data "as
  is" plus...
• ... separate content from presentation
• ... tag your data (take a lift in the info
  hierarchy)
• ... use a self-describing, semistructured data
  format (XML)
• Knowledge-based archival now add ...
• ... conceptual level information
• ... integrity constraints
• ... explanations/derivation rules
• archiving only results yf(x) vs. archiving the
  rules/function "f" (e.g. f the
  Florida procedure...)
• gt employ knowledge representation languages

66
Knowledge-Based Persistent Archive
Ingest Services
Management
Access Services
Knowledge or Topic-Based Query / Browse
Knowledge Repository for Rules
Relationships Between Concepts
XTM DTD
Knowledge
Rules - KQL
(Topic Maps / Model-based Access)
Information Repository
Attribute- based Query
Attributes Semantics
SDLIP
Information
XML DTD
(Data Handling System - SRB / FTP / HTTP)
Data
Fields Containers Folders
Storage (Replicas, Persistent IDs)
Grids
Feature-based Query
MCAT/HDF
67
Knowledge-Based Archival Senate Example

• Data provider says
• Please archive all records of legislative
  activities of the 106th senate!
• Integrity constraints, eg
• (1) senators_with_file UNION (sponsor,
  cosponsors, submitted_by)
• (2) senators sponsors co-sponsors
• Violation
• the rhs is a SUPERSET of the lhs !
• Exceptions
• (Chafee, John), (Gramm, Phil), (Miller, Zell)
• (Possible) Explanations
• senators who joined (Zell), passed away (Chafee),
  were forgotten (Gramm)!?
• Checking ICs
• IF sponsor(X), not senator(X) THEN
  ADD(exception_log, missing_senator_info(X))
• IF condition THEN action
  
• Action LOG, WARN,
  ABORT, ...

68
NARA Herbicides Collection Introduction
69
The Herbicides Collection - input
From EBCDIC tapes

• 6507213207565 260404040 0400000000D000000004
  8 0000000000000000000000000000
• 6507243207565 260606060 0600000000D000000007
  2 0000000000000000000000000000
• 6507253207565 260606060 0600000000D000000007
  2 0000000000000000000000000000
• 6507263207565 260606060 0600000000D000000007
  2 0000000000000000000000000000
• 6507273207565 260606060 0600000000D000000007
  2 0000000000000000000000000000
• 6507283207565 260505050 0500000000D000000006
  0 0000000000000000000000000000
• 6507293207565 260404040 0400000000D000000004
  8 0000000000000000000000000000
• 6508022022365 060202020 0100000000C000000001
  2 00000000000000000000000000001A
  AS890255 000000
• 6508022022365
  1B
  AS940140 000000
• 6508042022365 060202020 0060000000C000000000
  7B 00000000000000000000000000001A
  AS925205 000000
• 6508042022365
  1B
  AS970065 000000
• 6508062022365 060202020 0040000000C000000000
  4H 00000000000000000000000000001A
  BS290320 000000
• 6508062022365
  1B
  BS275298 000000
• 6508073207565 260202020 0200000000D000000002
  4 00000000000000000000000000001A
  YT080110 000000
• 6508073207565
  1B
  YT110060 000000
• 6508113207565 260202020 0200000000D000000002
  4 0000000000000000000000000000
• 6508123207565 260202020 0200000000D000000002
  4 0000000000000000000000000000
• 6508151022465 020202020 0080000000C000000000
  9F 00000000000000000000000000001A
  YD350155 000000
• 6508151022465
  1B
  YD450150

70
The Herbicides Collection - preservation
Converted to XML
ltYEARgtltyearnumgt66lt/yearnumgt ltMONTHgtltmonthnumgt01lt/m
onthnumgt ltDATEgtltdatenumgt01lt/datenumgt ltMISSIONgtltnum
gt206866lt/numgt ltRUNgtltcodegtAlt/codegt ltctzgt3lt/ctzgtltm
ultigtlt/multigtltprovgt27lt/provgt ltaircraftsgt ltsch
eduledgt02lt/scheduledgtltairbornegt02lt/airbornegtltprodu
ctivegt02lt/productivegt lt/aircraftsgt ltagentgtOlt/a
gentgtltgalgt02000lt/galgtlthitsgt0lt/hitsgt ltabortsgt lt
maintenancegt0lt/maintenancegtltweathergt0lt/weathergtltba
ttle_damagegt0lt/battle_damagegtltothergt0lt/othergt lt/
abortsgt lttypegtDlt/typegtltareagt024lt/areagtltrsultgtlt/r
sultgt ltUTMgt ltutmidgt1Alt/utmidgt ltutm_coorgtYS
240780lt/utm_coorgt lt/UTMgt ltUTMgt ltutmidgt1Blt/u
tmidgt ltutm_coorgtYS290630lt/utm_coorgt lt/UTMgtlt/R
UNgt ltRUNgtltcodegtBlt/codegt ltctzgt3lt/ctzgtltmultigtlt/mul
tigtltprovgt27lt/provgt ltaircraftsgt ltscheduledgt02lt
/scheduledgtltairbornegt02lt/airbornegtltproductivegt02lt/
productivegt lt/aircraftsgt ltagentgtOlt/agentgtltgalgt
02000lt/galgtlthitsgt0Alt/hitsgt ltabortsgt ltmaintenan
cegt0lt/maintenancegtltweathergt0lt/weathergtltbattle_dama
gegt0lt/battle_damagegtltothergt0lt/othergt lt/abortsgt
lttypegtDlt/typegtltareagt024lt/areagtltrsultgtlt/rsultgt
MAPPING
71
From Geography Markup to Rendering
lt?xml version"1.0" encoding"iso-8859-1"?gt ltrsgt lt
rgtltnamegtHorton Plazalt/namegtltURLgtlt/URLgtltlabelposgt41
.46,77.51lt/labelposgtltcgt5076,1540 4986,1540
4895,1539 4803,1539 4715,1539 4622,1539 4534,1538
4534,1641 4534,1745 4534,1856 4622,1856 4711,1856
4800,1856 4893,1855 4984,1855 5075,1854 5075,1749
5076,1646 lt/cgtlt/rgt ltrgtltnamegtGaslamplt/namegtltURLgtlt/U
RLgtltlabelposgt44.60,83.00lt/labelposgtltcgt5162,1013
5084,1057 5083,1116 5081,1222 5079,1326 5079,1433
5076,1540 5076,1646 5075,1749 5075,1854 5167,1854
5257,1855 5257,1750 5259,1647 5260,1541 5262,1434
5262,1328 5263,1222 5263,1013 lt/cgtlt/rgt . . .
XML encoding of geographic features (such as GML)
72
XML Map Viewer for the Herbicides Collection
73
Conclusion

• Necessity Requirements of a Virtual Data Grid
• SRB a proven solution
• It is an existing middle-ware
• Field-tested in multiple projects
• Proven Scalability users, data resources
• New element of data grid knowledge management
• Working solutions
• BIRN the first real data grid complete with
  knowledge management and cross-ontology bridges
• Web services, to expose grid functionality in a
  uniform way
• Archiving data, information and knowledge as a
  gridactivity
• www.npaci.edu/DICE/