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Changes for page kg-spatial-search

Last modified by oschmid on 2023/08/22 11:23
From version 15.1
edited by oschmid
on 2023/08/22 09:52
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To version 7.2
edited by oschmid
on 2023/08/22 08:58
Change comment: There is no comment for this version

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22 22  
23 23  = What data is registered in the spatial search? =
24 24  
25 -Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries. Please also note that currently only datasets can be registered for which the spatial location information is regarded to be non-sensitive from a GDPR perspective.
25 +Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries.
26 26  
27 27  = How do I query the KG Spatial Search? =
28 28  
29 -Simply use the API endpoint at [[https:~~/~~/spatial.kg.ebrains.eu/api/spatial_objects>>https://spatial.kg.ebrains.eu/spatial_objects]] by running queries according to the following examples:
29 +Go to
30 30  
31 -== Get started: Query by a "bounding box" (hyperrectangle) ==
31 +Theoretically, all data which is anchored in their respective coordinate space can be registered in the KG Spatial Search. We currently provide ingestion pipelines for data annotated with the[[ locareJSON format>>https://github.com/blixilla/LocareJSON]]. Please note that complex, planar and volumetric geometries such as polyhedrons, spheres, etc. are translated into point clouds of a configurable density during the ingestion process to ensure better performance at query time for the cost for potential false-negatives in highly fine-granular queries.
32 32  
33 -{{code language="bash" layout="LINENUMBERS"}}
34 -curl -X 'POST' \
35 - 'https://spatial.kg.ebrains.eu/api/spatial_objects' \
36 - -H 'accept: application/json' \
37 - -H 'Content-Type: application/json' \
38 - -d '{
39 - "filters": "inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"})"
40 -}'
41 -{{/code}}
42 42  
43 -As you can see, you're sending a POST request to the endpoint at **https:~/~/spatial.kg.ebrains.eu/spatial-search/cores/ebrains/spatial_objects  **with a payload defining a geometry of interest. In this case, we're looking for all objects that are **inside **a **hyperrectangle **defined by its lowest and highest point in the coordinate system of the coordinate space **AMB-CCF_v3-RAS**. This query will return you an array of ids of the objects located within the geometry which you then can use to conveniently query the KG either through the [[Instance API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/2%20Advanced/getInstancesByIds]] or the [[Query API>>https://core.kg.ebrains.eu/swagger-ui/index.html#/1%20Basic/runDynamicQuery]] to access detailed meta information.
44 44  
45 -== Query by hypersphere ==
46 -
47 -Alongside the possibility to use hyperrectangles for querying the spatial search, you can also use hyperspheres:
48 -
49 -{{code language="javascript" layout="LINENUMBERS"}}
50 -{
51 - "filters": "inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"})"
52 -}
53 -{{/code}}
54 -
55 -It is defined by the center of the sphere with its coordinates, the radius in coordinate units and the coordinate space of the given coordinates (in this case again AMB-CCF_v3-RAS)
56 -
57 -== Advanced use: Union geometries for complex queries ==
58 -
59 -To build arbitrarily complex geometries, it is possible to combine hyperrectangles and hyperspheres via (nested) unions:
60 -
61 -{{code language="javascript" layout="LINENUMBERS"}}
62 -{
63 - "filters": "union(union(inside(hyperrectangle{[50.0,146.0,227.0], [51.0, 149.0, 228.0], \"AMB-CCF_v3-RAS\"}), inside(hyperrectangle{[120.0,146.0,227.0], [150.0, 180.0, 300.0], \"AMB-CCF_v3-RAS\"})),inside(hypersphere{[50.0,146.0,227.0], 2, \"AMB-CCF_v3-RAS\"}))"
64 -}
65 -{{/code}}
66 -
67 -
68 -= Technical details of the ingestion pipeline =
69 -
70 -The current pipeline is regularly parsing locareJSON files registered in the KG.
71 -
72 -* Points are translated to the data structure of spatial search without manipulation
73 -* Polyhedrons are translated via the [[vtk>>https://pypi.org/project/vtk/]] library into a point cloud of (currently) a density of 1 coordinate unit (by ray-casting on an obb-tree)
74 -* Other geometries are work-in-progress
75 -
76 -The resulting files (all representing point-clouds) as well as the space definitions are uploaded to the [[bucket of this collab>>https://data-proxy.ebrains.eu/kg-spatial-search]]. A cron job running on the spatial search server pulls the files from the repo and rebuilds the databases index in regular intervals.
77 -
78 -
79 79  
80 80  )))
81 81