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

[S 1formation )n systems. nodels, and rge objects, tial objects. presents a alyzing the ect Related >s. We then lel that uses s. We also pport large ness of the and further tion, GIS Video 1ds pectrum ser 1. INTRODUCTION In the past few years, a substantial development has been going on in the field of managing large spatial objects such as digital imagery, digital terrain model and scanned maps, mainly due to the interest of building multi-media spatial information systems and global environmental information systems. The development can be roughly classified into two categories. The first category is through system integration which uses two or more different systems, such as image processing system to handle image objects and Data Base Management System(DBMS) to handle non image objects such as text and graphics. Examples can be found in Chang[1990], Zhou Q.[1989], Wegener[1989], and Zhou[1991]]. The second category is through next generation data base systems using Abstract Data Type(ADT) or object oriented data model to handle large objects. These systems include Lohman[1989], Orenstein[1989], Deux [1990], Gupta[1992], and Stonebraker [1993]. However, there are no generally accepted solutions in GISs at this time. With the first method, two systems are loosely integrated. Large objects in the image processing systems are processed independently and the results are converted into the DBMS to perform GIS operations. This not only limits the use of DBMS for large object management, but also makes the data processing unnecessary complicated and time consuming because of multi data conversions. With the second method, all large objects are treated as long binary data strings with little semantics and data abstraction associated with them. This is not only inefficient for data processing because the whole data set may need to be read, written and processed together, but it also makes many kinds of interactive data processing impossible. In this paper we will analyze large object contents, highlight their special features, and develop an object oriented model to support them in GISs, using digital images as examples. We will also investigate their query patterns and present several methods that can be used to reduce the amount of data, to improve data retrieval efficiency, to speed up data query and to better Support browsing. We will then use several Practical GIS query examples to show the performance improvement upon using different techniques. We conclude the paper with some discussions on system performance and future research issues. 2 . LARGE SPATIAL OBJECTS Large spatial objects are often represented by multi-dimensional matrices using long unstructured byte strings that are often stored and transmitted entirely. More precisely large objects consist of a list of small items and long data strings. The list of small items will be used to interpret the data format and meaning of the unformatted long data strings following it. For image data, these small items may be the image header; For Digital Terrein Model(DTM) data, these may be the name of the region, the coordinates of the origin, resolution, precision, etc. These small items are often mandatory and are used to interpret long data string for display and process, and/or to identify and distinguish one data string from others. We call these small items direct related attribute data(DRAD). While DRAD is indispensable, other formatted attribute data describing the contents and features of large spatial objects is generally not mandatory. For digital imagery, this data may be histogram, color map, and interpretation results from the original image data; For DTM, it may be the contour line, the slope and visibility data. This data is often the result of data calibration, interpretation, processing and analyses. We call this data derived attribute data(DAD). The DAD data is per se redundant because it is just another form of information presented in the source data. Usually DAD is very difficult and/or time consuming to derive. In GIS, it is desirable to store DAD in the database because DAD is high level information and can be used to answer most GIS queries. In addition we prefer to integrate DAD into GIS databases because DBMS then can be used to manage DAD. Because DAD and DRAD is much simpler than the raw data and sometime they may be well modeled by relational data model, many researchers use this technique to handle large objects. They store DAD and DRAD in RDMS, while manipulating the long string data through a link between the relational table and operating system files[Zhou, 1991]. This approach may work for simple applications, but it has several serious drawbacks. First, the DAD and DRAD can be semantic rich and complicated (for example geometry and topology data) because of the amount of information embodied in a large object. Second, because of two data bases used are independent, it is very difficult to maintain data integrity and perform transaction management. Third, the relational data model lacks the power to define the semantics inherited in the large objects and the methods needed to process large objects. It is indeed not too much 213

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