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Big Data Analytics WG: Use Case Array Databases
RDA 4th Plenary 2014-sep-22, Amsterdam, The Netherlands Peter Baumann Jacobs University | rasdaman GmbH baumann@rasdaman.com
Array Databases: Practice Proven with rasdaman
from simple data access to agile analytics
- strictly based on open OGC Big Geo Data standards
130+ TB databases, 2D, 3D x/y/z & x/y/t, 4D x/y/z/t timeseries
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Structural Variety in Big Data
Stock trading: 1-D sequences (i.e., arrays) Social networks: large, homogeneous graphs Ontologies: small, heterogeneous graphs Climate modelling: 4D/5D arrays Satellite imagery: 2D/3D arrays (+irregularity) Genome: long string arrays Particle physics: sets of events Bio taxonomies: hierarchies (such as XML) Documents: key/value stores = sets of unique identifiers + whatever etc.
declarative array operations
select id, encode(scene.band1-scene.band2)/(scene.nband1+scene.band2)), „image/tiff“ ) from LandsatScenes where acquired between „1990-06-01“ and „1990-06-30“ and avg( scene.band3-scene.band4)/(scene.band3+scene.band4)) > 0
single query distributed to 1,000+ cloud nodes
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Recent Progress: ISO Array SQL
ISO 9075 Part 15: SQL/MDA
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
rasdaman: Agile Array Analytics
„raster data manager“: SQL + n-D raster objects
select img.green[x0:x1,y0:y1] > 130 from LandsatArchive as img where avg_cells( img.nir ) < 17
- resolved by ISO SQL WG in June 2014
n-D arrays as attributes
create table LandsatScenes( id: integer not null, acquired: date, scene: row( band1: integer, ..., band7: integer ) array [ 0:4999,0:4999] )
源自文库
Scalable parallel “tile streaming” architecture In operational use
- OGC Web Coverage Service Core Reference Implementation
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
RDA 4th Plenary 2014-sep-22, Amsterdam, The Netherlands Peter Baumann Jacobs University | rasdaman GmbH baumann@rasdaman.com
Array Databases: Practice Proven with rasdaman
from simple data access to agile analytics
- strictly based on open OGC Big Geo Data standards
130+ TB databases, 2D, 3D x/y/z & x/y/t, 4D x/y/z/t timeseries
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Structural Variety in Big Data
Stock trading: 1-D sequences (i.e., arrays) Social networks: large, homogeneous graphs Ontologies: small, heterogeneous graphs Climate modelling: 4D/5D arrays Satellite imagery: 2D/3D arrays (+irregularity) Genome: long string arrays Particle physics: sets of events Bio taxonomies: hierarchies (such as XML) Documents: key/value stores = sets of unique identifiers + whatever etc.
declarative array operations
select id, encode(scene.band1-scene.band2)/(scene.nband1+scene.band2)), „image/tiff“ ) from LandsatScenes where acquired between „1990-06-01“ and „1990-06-30“ and avg( scene.band3-scene.band4)/(scene.band3+scene.band4)) > 0
single query distributed to 1,000+ cloud nodes
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
Recent Progress: ISO Array SQL
ISO 9075 Part 15: SQL/MDA
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann
rasdaman: Agile Array Analytics
„raster data manager“: SQL + n-D raster objects
select img.green[x0:x1,y0:y1] > 130 from LandsatArchive as img where avg_cells( img.nir ) < 17
- resolved by ISO SQL WG in June 2014
n-D arrays as attributes
create table LandsatScenes( id: integer not null, acquired: date, scene: row( band1: integer, ..., band7: integer ) array [ 0:4999,0:4999] )
源自文库
Scalable parallel “tile streaming” architecture In operational use
- OGC Web Coverage Service Core Reference Implementation
Big Data: Arrays :: RDA 4th Plenary :: © 2014 P. Baumann