The need for benchmarks for geographical data Frans Knibbe Geodan Introduction.

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Presentation transcript:

The need for benchmarks for geographical data Frans Knibbe Geodan Introduction

Geographical data Data about where things are in relation to the earth’s surface. Handling this kind of data is well established in relational databases. This should also be the case for RDF databases. Two broad categories of geographical data: Textual data Numerical data

Textual geographical data Addresses: Science Park XH Amsterdam The Netherlands Geographical names (toponyms): ‘Amsterdam’ ‘My house’ ‘Europe’ No special datatypes are needed for storage.

Numerical geographical data Raster data: Vector data:

Raster data in SQL databases Support usually follows support for vector data. Rectangles of grid cells are stored as single objects. Other table columns store metadata needed to correcly interpret the raw raster values.

Vector data in SQL databases Major RDBMSs have vector data support: Oracle, IBM, Microsoft, PostgreSQL, SQLite,... Standardization was helped by the OGC Simple Features standard (version 1 in 1998), which specifies the geometry datatype and the operations that this datatype can support. Examples of geometry: point, line, polygon, multiline, multipolygon,... Examples of geometry functions: calculate distance or area, transform coordinates, add or substract geometries, filter on topological relationships,...

Topological relationship example 1

Topological relationship example 2

Vector data in the semweb (1) 2003: W3C basic geo vocabulary: Schema.org: GeoShape (box, circle, line, polygon) 2009: NeoGeo: ex:way_1 rdf:type ngeo:LineString. ex:way_1 ngeo:posList ( [geo:lat " "; geo:long " "] [geo:lat " "; geo:long " "] ).

Vector data in the semweb (2) 2012: GeoSPARQL (OGC): SELECT ?f WHERE { ?f my:hasPointGeometry ?fGeom. ?fGeom geo:asWKT ?fWKT. FILTER (geof:sfWithin(?fWKT, " Polygon (( , , , , ))"^^geo:wktLiteral)) } Current situation: Vocabularies seem to be converging, OGC and W3C are considering cooperation, software implementations are emerging.

Why benchmark geographical data support?

Geography needs the dataweb: Improved handling of dataset metadata Global semantics Easier discovery and access of data The dataweb needs geographical data: More data More applications and services Improved data connectivity through topological relationships

Things to benchmark Support for inserting and updating geometry. Which geometry types are supported? Which geometry notations are supported? Support for multiple Coordinate Reference Systems. Support for functions that operate on geometry. Performance of all supported operations. Validation of results. How can we make this happen? Lehigh University Benchmark has seen a first extension for spatial data. Continue from there? extension for spatial data

The end

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