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EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann The EarthServer initiative: towards Agile Big Data Services 2nd GEOSS Science and Technology Stakeholder Workshop Bonn, Germany, 2012-aug-29 Peter Baumann Jacobs University | rasdaman GmbH Bremen, Germany
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann About the Presenter Professor of CS, Jacobs University Head, Large-Scale Scientific Information Systems research group Main outcome so far: rasdaman first „Big Raster Data Analytics“ server Standardization OGC: chair of raster-relevant working groups, editor of 12+ standards & candidate standards ISO: working on Raster („Array“) SQL INSPIRE: Invited expert for coverages
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Roadmap OGC standards rasdaman EarthServer EarthServer & GEOSS Conclusions
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Feature and Coverage Data Standards Core element in OGC: geographic feature = abstraction of a real world phenomenon associated with a location relative to Earth Special kind of feature: coverage = space-time varying multi-dimensional phenomenon Typical representative: raster image...but there is more! Typically, coverages are Big Data
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann «FeatureType» Abstract Coverage MultiSolid Coverage MultiSurface Coverage MultiCurve Coverage MultiPoint Coverage Discrete Coverage Continuous Coverage as per GML Rectified GridCoverage Referenceable GridCoverage Grid Coverage all n-D New subtypes possible Coverage Types
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Coverage Encoding Pure GML: complete coverage represented by GML Special Format: other suitable file format (ex: MIME type “image/tiff”) Multipart-Mixed: multipart MIME, type “multipart/mixed” GML Coverage Domain set Range type Range set App Metadata GML Coverage Domain set Range type xlink App Metadata NetCDF file NetCDF Domain set Range type Range set App Metadata GeoTIFF Range type Range set 6
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann featurecoverage data WMS imagesdata meta data WCPS WCS-T WCS FE WFS-T WFS CQL CS-T CS-W Core OGC Service Standards WMS "portrays spatial data” pictures WCS "provides data + descriptions; data with original semantics, may be interpreted, extrapolated, etc.“ [09-110r4] … … … 7
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Web Coverage Service (WCS) Core: Simple & efficient access to multi-dimensional coverages subset = trim | slice WCS Extensions for additional functionality facets “band extraction”, scaling, reprojection, interpolation, query language,... Application Profiles define domain-oriented bundling 8
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Raster Query Language: ad-hoc navigation, extraction, aggregation, analytics Time series Image processing Summary data Sensor fusion & pattern mining Web Coverage Processing Service (WCPS)
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Scalable On-Demand Processing for the Earth Sciences EU funded, 3 years, 5.85 mEUR Platform: rasdaman (Array Analytics server) Distributed query processing, integrated data/metadata search, 3D clients Strictly open standards: OGC WMS+WCS+WCPS; W3C Xquery; X3D 6 * 100+ TB databases for all Earth sciences + planetary science EarthServer: Big Earth Data Analytics
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Array DBMS for massive n-D raster data new database attribute type: array Data integration: rasters stored in standard database Extending ISO SQL with array operators: “tile streaming” architecture n-D array set of n-D tiles extensive optimization, hw/sw parallelization In operational use dozen-Terabyte objects Analytics queries in 50 ms on laptop The rasdaman Raster Analytics Server select img.green[x0:x1,y0:y1] > 130 from LandsatArchive as img
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Value-Added Satellite Image Archive [Diedrich et al 2001]
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann WCPS peer-to-peer cloud each node accepts all requests Incoming node distributes query, semantics based Manifold optimization criteria for $a in ( A ), $b in ( B ) return encode( ( ($a.nir - $a.red) / ($a.nir + $a.red) - ($b.nir - $b.red) / ($b.nir + $b.red) ), “ HDF5 “ ) coverage A for $b in ( B ) return encode( ($b.nir - $b.red) / ($b.nir + $b.red), “ array-compressed “ ) for $a in ( A ) return encode( ($a.nir - $a.red) / ($a.nir + $a.red), “ array-compressed “ ) rasdaman: Distributed Query Processing coverage B [Owonibi 2012]
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann EarthServer Contribution to GEOSS Integrated n-D coverage data / metadata search Smooth integration with Broker [Nativi, Mazzetti 2012]
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Integrated n-D coverage data / metadata search Smooth integration with Broker EarthServer Contribution to GEOSS Including „reverse lookup“ queries: „give me metadata for data with specific properties“ Also integration with MapServer, GDAL,... Scalable n-D interfaces, based on OGC standards Working „in situ“on existing archives; no copying! Flexible ad-hoc processing & filtering Through OGC standardized query language nD visual Web clients 1D diagrams, 2D maps, 3D data cubes, 3D timeseries sets,... Dynymically composed from query results
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Conclusion Sensor, image, & statistics data = a main source of Big Data in Earth Sciences Petrol industry has „more bytes than barrels“ OGC standards offer common platform spatio-temporal coverages – a unified, cross-domain data model Web Coverage Service suite – from simple download to flexible analytics EarthServer can contribute Agile Analytics to GEOSS OGC coverage standards rasdaman technology
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann SWE O&M and SensorML (+ friends): high flexibility to accommodate virtually any data structure → upstream integration GMLCOV and WCS (+WCPS): one generic schema for all coverage types; scalable; versatile processing → downstream services coverage server O&M + SensorML GMLCOV + WCS Semantic Web Integration of OGC WCS and SWE
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann VAROS (contd.d)
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann The Integrated Geo Warehouse 1D 2D 3D Compprehensive geophysics data mgmt seismic measurement, borehole data, geophone data, geo tomograms, stratigraphy layers, geological models,... + annotations + meta data nD
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Let’s Take a Closer Look... Divergent access patterns for ingest and retrieval Alternative 1: simple access service, let client chisel result Alternative 2: Deliver to exact needs o no bandwidth waste, higher quality of service Server must mediate between access patterns (...later more) Intelligent access interfaces help t
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann interfaces: OGC or API Server: OGC interfaces as servlets: WCS 2.0, WCPS 1.0, WPS 1.0 Server engine: C++ Bindings to GDAL, MapServer, ERDAS (to be extended) Ex: VAROS project (ESA) Commercial client, ChartLink Open-source server, rasdaman petascope request translator rasdaman engine metadata standard database system WCS+WCPSWPS+WCPS System Architecture
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Just-In-Time Compilation Observation: interpreted mode slows down Approach: cluster suitable operations compile & dynamically bind Benefit: Speed up complex, repeated operations Variation: compile code for GPU Times [ms] for * n ops [Jucovschi, Stancu-Mara 2008] select x*x*...*x from float_matrix as x
EarthServer :: GEOSS 2012, Bonn :: ©2012 Peter Baumann Query Optimization – Ex. 1
The Next Generation Network Enabled Weather (NNEW) SWIM Application Asia/Pacific AMHS/SWIM Workshop Chaing Mai, Thailand March 5-7, 2012 Tom McParland,
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NASA and The Semantic Web Naveen Ashish Research Institute for Advanced Computer Science NASA Ames Research Center.
Neil Chue Hong Project Manager, EPCC OGSA-DAI data access and integration NERC GridGIS workshop eSI, 1 February.
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SDI Standards Hans Voss Fraunhofer-Gesellschaft e.V.
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Geospatial Interoperability and the Open Geospatial Consortium Mike Jackson Centre for Geospatial Science University of Nottingham.
1 Towards an Open Service Framework for Cloud-based Knowledge Discovery Domenico Talia ICAR-CNR & UNIVERSITY OF CALABRIA, Italy Cloud.
Routemap to derive ISO models from BUFR Why do we need both ISO and BUFR models? –The BUFR data model is very large – much larger in principle than most.
WCS :: OGC Bonn 2005-nov-09 Some GALEON Results for WCS OGC, Bonn, 2005-nov-09 Peter Baumann International University Bremen, rasdaman GmbH
RMS and Scheduling for Future Generation Grids Ramin Yahyapour University Dortmund Leader CoreGRID Institute on Resource Management and Scheduling CoreGRID.
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Spatial Data Infrastructure Concepts and Components Douglas Nebert U.S. Federal Geographic Data Committee Secretariat August 2009.
BIOMEDICAL DATA INTEGRATION BASED ON METAQUERIER ARCHITECTURE GROUP MEMBERS -NAIEEM KHAN -EUSUF ABDULLAH MIM -M SAMIULLAH CHOWDHURY ADVISOR : KHONDKER.
1 Online Science the New Computational Science Jim Gray Microsoft Research Alex Szalay Johns Hopkins.
GEOSS ADC Architecture Workshop Initial Operating Capability (IOC) Doug Nebert U.S. Geological Survey February 4, 2008.
1 NASA Sensor Web Activities Martha Maiden Program Executive Earth Science Data Systems NASA Headquarters CEOS WGISS-23 Hanoi, Vietnam.
October 2007 Data integration architectures and methodologies for the Life Sciences Alexandra Poulovassilis, Birkbeck, U. of London.
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