TY - GEN
T1 - Point cloud databases
AU - Dobos, László
AU - Csabai, István
AU - Szalai-Gindl, János M.
AU - Budavári, Tamás
AU - Szalay, Alexander S.
PY - 2014
Y1 - 2014
N2 - We introduce the concept of the point cloud database, a new kind of database system aimed primarily towards scientific applications. Many scientific observations, experiments, feature extraction algorithms and large-scale simulations produce enormous amounts of data that are better represented as sparse (but often highly-clustered) points in a k-dimensional (κ ≲ 10) metric space than on a multidimensional grid. Dimensionality reduction techniques, such as principal components, are also widely-used to project high dimensional data into similarly low dimensional spaces. Analysis techniques developed to work on multi-dimensional data points are usually implemented as in-memory algorithms and need to be modified to work in distributed cluster environments and on large amounts of disk-resident data. We conclude that the relational model, with certain additions, is appropriate for point clouds, but point cloud databases must also provide unique set of spatial search and proximity join operators, indexing schemes, and query language constructs that make them a distinct class of database systems.
AB - We introduce the concept of the point cloud database, a new kind of database system aimed primarily towards scientific applications. Many scientific observations, experiments, feature extraction algorithms and large-scale simulations produce enormous amounts of data that are better represented as sparse (but often highly-clustered) points in a k-dimensional (κ ≲ 10) metric space than on a multidimensional grid. Dimensionality reduction techniques, such as principal components, are also widely-used to project high dimensional data into similarly low dimensional spaces. Analysis techniques developed to work on multi-dimensional data points are usually implemented as in-memory algorithms and need to be modified to work in distributed cluster environments and on large amounts of disk-resident data. We conclude that the relational model, with certain additions, is appropriate for point clouds, but point cloud databases must also provide unique set of spatial search and proximity join operators, indexing schemes, and query language constructs that make them a distinct class of database systems.
KW - Multi-dimensional database
KW - Proximity join
KW - Spatial indexing
UR - http://www.scopus.com/inward/record.url?scp=84904438110&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904438110&partnerID=8YFLogxK
U2 - 10.1145/2618243.2618275
DO - 10.1145/2618243.2618275
M3 - Conference contribution
AN - SCOPUS:84904438110
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 -