TY - GEN
T1 - Efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh
AU - Kondor, Dániel
AU - Dobos, László
AU - Csabai, István
AU - Bodor, András
AU - Vattay, Gábor
AU - Budavári, Tamás
AU - Szalay, Alexander S.
PY - 2014
Y1 - 2014
N2 - We present a case study about the spatial indexing and regional classification of billions of geographic coordinates from geo-tagged social network data using Hierarchical TriangularMesh (HTM) implemented for Microsoft SQL Server. Due to the lack of certain features of the HTM library, we use it in conjunction with the GIS functions of SQL Server to significantly increase the efficiency of pre-filtering of spatial filter and join queries. For example, we implemented a new algorithm to compute the HTM tessellation of complex geographic regions and precomputed the intersections of HTM triangles and geographic regions for faster falsepositive filtering. With full control over the index structure, HTM-based pre-filtering of simple containment searches outperforms SQL Server spatial indices by a factor of ten and HTM-based spatial joins run about a hundred times faster.
AB - We present a case study about the spatial indexing and regional classification of billions of geographic coordinates from geo-tagged social network data using Hierarchical TriangularMesh (HTM) implemented for Microsoft SQL Server. Due to the lack of certain features of the HTM library, we use it in conjunction with the GIS functions of SQL Server to significantly increase the efficiency of pre-filtering of spatial filter and join queries. For example, we implemented a new algorithm to compute the HTM tessellation of complex geographic regions and precomputed the intersections of HTM triangles and geographic regions for faster falsepositive filtering. With full control over the index structure, HTM-based pre-filtering of simple containment searches outperforms SQL Server spatial indices by a factor of ten and HTM-based spatial joins run about a hundred times faster.
UR - http://www.scopus.com/inward/record.url?scp=84904438106&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904438106&partnerID=8YFLogxK
U2 - 10.1145/2618243.2618245
DO - 10.1145/2618243.2618245
M3 - Conference contribution
AN - SCOPUS:84904438106
SN - 9781450327220
T3 - ACM International Conference Proceeding Series
BT - SSDBM 2014 - Proceedings of the 26th International Conference on Scientific and Statistical Database Management
PB - Association for Computing Machinery
T2 - 26th International Conference on Scientific and Statistical Database Management, SSDBM 2014
Y2 - 30 June 2014 through 2 July 2014
ER -