TY - GEN
T1 - Geo-data fusion integrator for object-oriented spatiotemporal OLAP cubes
AU - Ngan, Chun Kit
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/24
Y1 - 2014/9/24
N2 - We first propose a Geo-Data Fusion Integrator. Specifically, we design a sequential-parallel-modularized (SPM) approach to integrate different datasets into a geo-data object, i.e., a multidimensional unified-OLAP cube, archived in a geo-data warehouse for decision-making analysis. Different datasets of geo-data objects are processed in parallel across multi-stages in sequence, and then integrated into a well-defined OLAP cube. Each SPM component is a self-contained, modularized unit that processes the data. The technical merits of this SPM approach include fast manipulations, error minimization, and easy maintenance. Second, to create a unified geo-data object, we extend the object-oriented spatialoral data model as a multidimensional OLAP cube, i.e., a Star-based Geo-Object-Oriented SpatiotEmporal (S-GOOSE) data model, which combines the advantages of both OLTP and OLAP approaches. This S-GOOSE data model is an object-relational-based cube that enables military operators to analyze unified geo-data objects from multiple dimensions, such as time, space, and location, to help them make a better decision on paths.
AB - We first propose a Geo-Data Fusion Integrator. Specifically, we design a sequential-parallel-modularized (SPM) approach to integrate different datasets into a geo-data object, i.e., a multidimensional unified-OLAP cube, archived in a geo-data warehouse for decision-making analysis. Different datasets of geo-data objects are processed in parallel across multi-stages in sequence, and then integrated into a well-defined OLAP cube. Each SPM component is a self-contained, modularized unit that processes the data. The technical merits of this SPM approach include fast manipulations, error minimization, and easy maintenance. Second, to create a unified geo-data object, we extend the object-oriented spatialoral data model as a multidimensional OLAP cube, i.e., a Star-based Geo-Object-Oriented SpatiotEmporal (S-GOOSE) data model, which combines the advantages of both OLTP and OLAP approaches. This S-GOOSE data model is an object-relational-based cube that enables military operators to analyze unified geo-data objects from multiple dimensions, such as time, space, and location, to help them make a better decision on paths.
UR - http://www.scopus.com/inward/record.url?scp=84908571889&partnerID=8YFLogxK
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U2 - 10.1109/COM.Geo.2014.5
DO - 10.1109/COM.Geo.2014.5
M3 - Conference contribution
AN - SCOPUS:84908571889
T3 - Proceedings - 5th International Conference on Computing for Geospatial Research and Application, COM.Geo 2014
SP - 51
EP - 53
BT - Proceedings - 5th International Conference on Computing for Geospatial Research and Application, COM.Geo 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computing for Geospatial Research and Application, COM.Geo 2014
Y2 - 4 August 2014 through 6 August 2014
ER -