Query Processing in 3D Spatial Databases: Experiences with Oracle Spatial 11g

Ravada, Siva; Kazar, Baris M.; Kothuri, Ravi

doi:10.1007/978-3-540-87395-2_10

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…The coordinates are not repeated here as in the Simple Feature. Oracle Spatial SDO TIN (Ravada et al, 2009) uses Triangle for storing TINs. The vertices are stored as SDO PC type and the triangles are stored as references to their three vertices.…”

Section: Tin Representationsmentioning

confidence: 99%

“…With the size of such point clouds exceeding the billion mark even for the smaller areas, difficulties arise in storing and managing these datasets. The existing DBMS solutions such as the Oracle Spatial SDO PC package (Ravada et al, 2009) and the PostgreSQL Point Cloud extension (Ramsey, 2013) provide only for the storage of point clouds i.e. unconnected points that are samples of the surface representing the Earth.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Data Structures for Storing Massive Tins in a DBMS

Kumar

Ledoux

Stoter

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m 2 . PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.

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Section: Tin Representationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Data Structures for Storing Massive Tins in a DBMS

Kumar

Ledoux

Stoter

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…However, these structures are still facing the issue of overlap between nodes. For instance, Oracle Database provides a 3D R-Tree structure to deal with 3D data (Murray, 2009;Ravada et al, 2009). However, when the R-Tree is extended into 3D space, the MBV of sibling nodes tends to frequently overlap, and MBVs among nodes can even contain other MBVs.…”

Section: Nearest Neighbour Searchmentioning

confidence: 99%

3d Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

Suhaibah

Ujang

Anton

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

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“…The surrounding built environment is constructed from the footprints of all building blocks within 200 meters. Benefiting from R-Tree indexing in Oracle Spatial 11g, the retrieval of all buildings from a given location is quite efficient [19]. The filled polygon is the user's 360 o visibility shape at that location.…”

Section: Dq Search Algorithmsmentioning

confidence: 99%

Effects of Variations in 3D Spatial Search Techniques on Mobile Query Speed vs. Accuracy

Yin¹,

Carswell²

2012

Web and Wireless Geographical Information Systems

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Abstract. This paper presents three Spatial Search Algorithms for determining the three dimensional visibility shape (threat dome) at a user's current location in a built environment. Users then utilize this 3D shape as their query "window" to retrieve information on only those objects visible within a spatial database. Visibility shape searching addresses the information overload problem by providing "Hidden Query Removal" functionality for mobile LBS. This functionality will be especially useful in the Web 4.0 era when trillions of micro-sensors become available for query through standard IP access.

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Query Processing in 3D Spatial Databases: Experiences with Oracle Spatial 11g

Cited by 12 publications

References 13 publications

Comparative Analysis of Data Structures for Storing Massive Tins in a DBMS

Comparative Analysis of Data Structures for Storing Massive Tins in a DBMS

3d Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

Effects of Variations in 3D Spatial Search Techniques on Mobile Query Speed vs. Accuracy

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