Efficient OLAP operations for spatial data using peano trees

Abstract

Online Analytical Processing (OLAP) is an important application of data warehouses. With more and more spatial data being collected, such as remotely sensed images, geographical information, digital sky survey data, efficient OLAP for spatial data is in great demand. In this paper, we build up a new data warehouse structure -PD-cube. With PD-cube, OLAP operations and queries can be efficiently implemented. All these are accomplished based on the fast logical operations of Peano Trees (P-Trees * ). One of the P-tree variations, Predicate P-tree, is used to efficiently reduce data accesses by filtering out "bit holes" consisting of consecutive 0's. Experiments show that OLAP operations can be executed much faster than with traditional OLAP methods.

Spatiotemporal Data Mining

“…A natural application of spatial mining was to perform image analysis, a subfield of multimedia. Early spatial stream works include efforts such as the classification and detection of outliers in stream satellite images (Pan et al 2003;Perrizo et al 2002;Wang et al 2003).…”

Spatial Data, Indexing Techniques

“…A natural application of spatial mining was to perform image analysis, a subfield of multimedia. Early spatial stream works include efforts such as the classification and detection of outliers in stream satellite images (Pan et al 2003;Perrizo et al 2002;Wang et al 2003).…”

Spatial Online Analytical Processing (SOLAP)

“…Other projects aim at improving spatial indexation, spatial aggregation or spatial operators (e.g. Gupta, Harinarayan, Rajaraman & Ullman, 1997;Han et al, 1998;Papadias, Kalnis, Zhang & Tao, 2001;Prasher & Zhou, 2004;Stefanovic et al, 2000;Wang, Pan, Ren, Cui, Ding & Perrizo, 2003;Zhang, Li, Rao, Yu, Chen & Liu, 2003;Zhou, Truffet & Han, 1999).…”