Systems for data processing, anaylsis and representation

Allam, Mosaad; Plunkett, Gordon

Bibliographic data

Monograph

Persistent identifier:: 1067490280

Title:: Systems for data processing, anaylsis and representation

Sub title:: ISPRS Commission II Symposium : June 6 - 10, Ottawa, Canada

Scope:: 1 Online-Ressource (XX, 530 Seiten)

Year of publication:: 1994

Place of publication:: Ottawa

Publisher of the original:: The Surveys, Mapping and Remote Sensing, Natural Resources Canada

Identifier (digital):: 1067490280

Illustration:: Illustrationen

Signature of the source:: ZS 312(30,2)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist aus dem Copyrightjahr ermittelt.

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Allam, Mosaad; Plunkett, Gordon

Corporations:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Adapter:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Founder of work:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Other corporate:: Symposium Systems for Data Processing, Analysis and Representation, 1994, Ottawa; International Society for Photogrammetry and Remote Sensing; International Society for Photogrammetry and Remote Sensing, Commission Instrumentation for Data Reduction and Analysis; Kanada, Surveys, Mapping and Remote Sensing Sector

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Wednesday, June 8, 1994]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: [Session F-2 WG II/3 - Technologies for Large-Volumes of Spatial Data - Part B]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: LARGE SPATIAL OBJECT HANDLING IN GEOGRAPHIC INFORMATION SYSTEMS Wenjin Zhou

Document type:: Monograph

Structure type:: Chapter

ul summary owing data howing the wowing data is our belief the success large spatial ODEL odel, let us mage object. XAD such as ation of the el precision, its used for meters used ity, etc.. The may be its of polygons sional array of rules to a rule like: less than 20, vater".) It is tional data e data (e.g. ta operation iandle by an | Wilkinson, Id define an th various methods to h class rules les[Hughes, iandle large 'e: first, the 'stract Data instructured 3]. It allows | manipulate ystem to be its users. In unformatted formation is nd method classes, and r to define sses[Deux et re raw data and provide em through d as a whole and no abstraction methods are provided to the users. In addition most commercial systems generally do not allow the query language to be extended to capture the semantics of large objects. Therefore new functions and operators defined by users may not be able to use internal data indexing and clustering techniques provided by the system to manage BLOBs and make the user defined methods or operators inefficient. To make large object manageable within a GIS environment, some new methods such as data compression, data ordering, controlled data redundancy, indexing and data browsing needs to be investigated, evaluated and implemented based on our data model and the possible GIS query patterns. This paper will mainly discuss these methods and present our implementation strategies. Using the object oriented data model, we have implemented a visual based schema browser to help users to define and modify the data base schema[Zhou and Wilkinson, 1993]. An example of the data base schema browser is shown in Figure 1. 5. LARGE SPATIAL OBJECT HANDLING METHODS Many current generation GISs have been designed to effectively manage point, line, surface and their attribute data. However, large spatial objects such as remotely sensed image, scanned document and DTM are series of large multidimensional arrays With various temporal, spatial and spectral characteristics. They are unformatted and quite often require long delayed processing in order to answer user's query or provide other useful information. Interactive, network based GIS queries may be very slow when large objects are concerned. Therefore efficient support of large objects must be designed into GISs. In order to manage large spatial objects effectively and efficiently, special techniques that allow applications to quickly store and retrieve large spatial object onto and from secondary memory devices such as a disk need to be developed. The traditional methods of storing large objects, such as imagery is linear allocation where the data is laid out linearly by a nested traversal of very dimension in a predefined order. The method may be optimized when the whole data of the large object is needed. Due to the limitation of the Current viewing system such as computer screen(e.g. 1000 by 1000 screen pixels vs. 20 by 20 K scanned Map) and the nature of data usage(e.g. quite often only a small part of data is used at a time), the linear type data storage can be very inefficient in many GIS applications. This situation will get worse when the dimension and size of data increase. 5.1 Data Compression One area that needs to be investigated is how we can reduce the amount of large object data while keeping most GIS queries satisfied. One obvious answer is data compression techniques such as reducing data quantization level or resolution, or using Run Length Encoding and JPEG compression [Wallace, 1991]. These techniques can significantly reduce the size of large objects and make large objects more manageable in data bases. The compression is one of the techniques for data reduction. It takes advantage of patterns in image data by removing redundancies and in effect, squeezing the data to maximize the information contained in each byte. Scanned maps, digital air photos and satellite images are considered good candidates for compression because of high correlation in the image data. The compression can be lossless(e.g. the decompressed data is identical to the original, Run Length compression is an example) or lossy (some information is lost. JPEG is an example for this type of compression). Lossy compression generally yields higher compression ratios than lossless. For some GIS applications, such as image display, this is a reasonable trade- off. However, some applications will not allow the lossy compression because of the precision requirement. Depending on the nature of image data, either lossless or lossy compression may significantly reduce the size of the image data to be managed. For scanned maps, lossless compression typically reduces the size by a factor of 2-10. This allows the database system to transfer more information with fewer bytes, thus making effective use of available band width. This can be very significant for network based GIS systems. Another method to perform data reduction is to reduce the data resolution and quantization level. This technique can significantly reduce the amount of data while satisfying most GIS queries. Table 1 gives the example of various data reduction methods and their data sizes before and after the data reduction. Since most GIS queries are "Gold Mining” or “Landscape” type that do not require detailed information in the first several queries, this technique can be significant for GIS applications. 215

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