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About 961 papers for “Object-based spatial database”
Yanlin Sun · 2004
The Geodatabase model was introduceed, using the Object-Oriented method and UML designs non- point source pollution spatial database of the Three-Gorge Region based on Geodatabase. The result indicates that the Object-Oriented method can do well in designing the spatial database by using Geodatabase model.
Cited by 0
Norman W. Paton, Alia I. Abdelmoty, Howard Williams · Innovations in GIS · 2023
This chapter addresses the provision of effective database support for spatial data management. It identifies a range of facilities which it would be desirable for a spatial database system to support, and evaluates a number of representative proposals against these requirements. The chapter focuses on architectural considerations which should be taken into account when developing spatial database systems. It presents an approach to spatial data management based upon the deductive object-oriented database system ROCK & ROLL. The chapter explains how such facilities should be catered for within database systems in order to obtain satisfactory performance, and compares the software architectures used by different proposals. It describes experience using a deductive object-oriented database system for managing spatial data. A novel approach to spatial data management based upon a deductive object-oriented database seeks to avoid premature commitment to a limited set of facilities, without sacrificing performance through the use of purely application independent tools.
Cited by 1
Wei Wu · 2004
This paper introduces Geodatabase model,designs non-point source pollution spatial database of the Three-Gorge Region based on Geodatabase with the object-oriented method and UML.The result indicates that the object-oriented method can do well in designing the spatial database with Geodatabase model.
Cited by 0
Paul Crowther, Jacky Hartnett · 2008
Abstract. One of the foundations of spatial analysis is the object. Objects can be many things varying from an actual feature which can be extracted from a scene to more abstract entities which are associated with those features. There are a variety of database structures which can be used to store data about spatial features. These include RDBMS (Relational Database Management Systems) OODBMS (Object Oriented Database Management Systems) and ORDBMS (Object Relational Database Management Systems). All of these have retrieval systems based on SQL (Structured Query Language). RDBMS systems store data in tables linked by foreign keys, data fields which two or more tables have in common. These form the basis of the majority of database systems in use and include products such as Oracle and Microsoft Access. They can be used with GIS via ODBC (Open DataBase Connectivity) and handle spatial objects by extending the data types available. OODBMS and ORDBMS such as PostgreSQL also store data in tables, but each of the tables represents an object (or object class) which spatial users are more comfortable with. The tables are arranged in a hierarchy with objects lower in the hierarchy inheriting attributes from those at higher levels. It is also possible to have objects which are composed of other objects. The dominant development methodology for object oriented applications is based on UML (Unified Modelling Language).
Cited by 0
Jiang Yao-peng · 2009
The object-oriented technology proposes new requirements for the development of spatial data model.Under this background,this paper reviews the development history of spatial data model,introduces the third generation spatial data model-Geodatabase based on Object-Oriented technology and standard relational database as a whole,and explicates the system structure and storage method in relational database of Geodatabase.After discussing the advantages and disadvantages of Geodatabase,this paper comes to a conclusion that the development trend of the future space data model is to realize the object-oriented space data physical model.
Cited by 2
Xiaochun Wu, Kai Wang, Zixuan Su et al. · 2012
With the technology of geographic information system (GIS) developing and the application of enterprise-level GIS gradually wide, it is necessary to build the high-availability spatial database. Now there are not commercial spatial database software which can realize the high-availability. The paper proposes the architecture of high-availability spatial database based on popular object-relation database software-Oracle and verify its feasibility by experiment. The paper provides a new idea to realize the high-availability spatial database.
Cited by 6
M. Molenaar · University of Twente Research Information · 1999
Topological object relationships in combination with object classification hierarchies appear to be fundamental in the definition of the aggregation rules for spatial objects. Such rules are essential building blocks for the construction of generalization procedures in spatial databases. A model for spatial database generalization can be formulated based on the syntax of the Formal Data Structure (FDS) as proposed in (Molenaar 1989). The syntax of the FDS will be formalized first, then database generalization procedures will be formulated with this syntax.
Cited by 0Open access
PC Crowther, JS Hartnett · eCite Digital Repository (University of Tasmania) · 2001
One of the foundations of spatial analysis is the object. Objects can be many things varying from an actual feature which can be extracted from a scene to more abstract entities which are associated with those features. There are a variety of database structures which can be used to store data about spatial features. These include RDBMS (Relational Database Management Systems) OODBMS (Object Oriented Database Management Systems) and ORDBMS (Object Relational Database Management Systems). All of these have retrieval systems based on SQL (Structured Query Language). RDBMS systems store data in tables linked by foreign keys, data fields which two or more tables have in common. These form the basis of the majority of database systems in use and include products such as Oracle and Microsoft Access. They can be used with GIS via ODBC (Open DataBase Connectivity) and handle spatial objects by extending the data types available. OODBMS and ORDBMS such as PostgreSQL also store data in tables, but each of the tables represents an object (or object class) which spatial users are more comfortable with. The tables are arranged in a hierarchy with objects lower in the hierarchy inheriting attributes from those at higher levels. It is also possible to have objects which are composed of other objects. The dominant development methodology for object oriented applications is based on UML (Unified Modelling Language). The aim of this paper is to compare the implementation of a GIS (Geographic Information System) database using RDBMS and OODBMS developed by normalisation and UML methodologies. To this end database schema of both types will be presented. These have been developed for an agricultural domain involving crop and field monitoring.
Cited by 3Open access
Won Bae Kim · 1995
I. NEXT-GENERATION DATABASE TECHNOLOGY. 1. Introduction to Part 1: Next-Generation Database Technology. 2. The OMG Model. 3. Object SQL---Language for the Design and Implementation of Object Databases. 4. OQL(C++): Extending C++ with an Object Query Capability. 5. C++ Bindings to an Object Database. 6. On View Support in Object-Oriented Database Systems. 7. Authorization in Object-Oriented Databases. 8. Query Processing in Object-Oriented Database Systems. 9. Physical Object Management. 10. Requirements for a Performance Benchmark for Object-Oriented Database Systems. 11. An Object-Oriented DBMS War Story: Developing a Genome Mapping Database in C++. 12. Where Object-Oriented DBMSs Should Do Better: A Critique Based on Early Experiences. 13. Object-Oriented Database Systems: Promises, Reality, and Future. 14. The POSC Solution to Managing E&P Data. 15. The Changing Database Standards Landscape. 16. Multimedia Information Systems: Issues and Approaches. 17. Spatial Data Models and Query Processing. 18. Spatial Data Structures. 19. Temporal Object-Oriented Databases: A Critical Comparison. 20. Cooperative Transactions for Multiuser Environments. 21. Active Database Systems. 22. Management of Uncertainty in Database Systems. 23. Distributed Databases. 24. Parallel Relational Database Systems. II. TECHNOLOGY FOR INTEROPERATING LEGACY DATABASES. 1. Introduction to Part 2: Technology for Interoperating Legacy Databases. 2. On Resolving Schematic Heterogeneity in Multidatabase Systems. 3. Query Processing in Multidatabase Systems. 4. Transaction Management in Multidatabase Systems. 5. Specification and Execution of Transactional Workflows. 6. Schema Architecture of the UniSQL/M Multidatabase System. 7. EDA/SQL. 8. Pegasus: A Heterogeneous Information Management System. 9. Overview of the ADDS System. 0201590980T04062001
Cited by 291
Wenbo Wang · 2007
This paper focuses on using conceptual model of spatial object for analyzing geographical data.The author tries to improve the existing hierarchy model of M-L-E(S,A) by taking into account of the advantages of object-relational database(ORDB).Entities in the modified model are created and connected by using class or inheritance methods.Connecting by references,there is no affiliation between layers in a map or entities.Therefore it is a better way of sharing and expressing spatial objects by employing ORDB rather than object-oriented database (OODB) or relational database(RDB).This paper also addresses the formula expression of the model and analysis of spatial entities,layers and maps based on ORDB.At the end,based on the method and characteristics of ORDB,the process of tracing map scales back to layers and entities are discussed.
Cited by 0
Yee Leung, Kwong‐Sak Leung, Jian He · International Journal of Geographical Information Systems · 1999
Abstract Unlike most of the current object-oriented geographical information systems (OOGISs) whose designs are based on the traditional spatial conceptual model emphasizing the processing of geometric features, the concept-based OOGIS proposed in this paper provides a spatial conceptual model which comprises rich spatial semantics fundamental to spatial analysis, and an object-oriented data model (OODM) which provides an appropriate and effective representation of the spatial conceptual model for efficient database management. By structuring the cognition of space through three interrelated hierarchies: namely the spatial conceptual hierarchy, the entity hierarchy and the feature hierarchy, the generic concept-based OOGIS renders an appropriate framework for the scientific investigation of space and the design of an efficient object-oriented database management system. In addition to its generic nature, the proposed OOGIS is in line withour commonsense conceptualization of space. Furthermore, it can entertain multiple-representations, and can facilitate data integration and generalization. The present investigation thus advances an effective way for OOGIS research in general and design in particular.
Cited by 24
Xiajun Jiang, Yuping Zhang, Dechang Pi · INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences · 2011
Datasets are the basis of the database benchmarks. They are very important to researches on database theories and applications. Datasets of the moving object database benchmarks record the spatial and temporal information of the moving objects. Based on the XML-based moving object data model, the algorithms generating moving object datasets are designed in this paper. The object number, object types, shape changes and moving fashions can be chosen through varying the parameters. During the initializing and moving process of moving objects, functions of collision checking are provided. For the moving regions, a special kind of polygon (convex region) and its generation algorithm are also discussed in this paper. Two typical queries, snapshot query and trajectory query, are implemented to validate the dataset generation algorithms and the XML-based data model.
Cited by 3
Mahmoud · Journal of Computer Science · 2012
Problem statement: A database that is optimized to store and query data that is related to objects in space, including points, lines and polygons is called spatial database. Identifying nearest neighbor object search is a vital part of spatial database. Many nearest neighbor search techniques such as Authenticated Multi-step NN (AMNN), Superseding Nearest Neighbor (SNN) search, Bayesian Nearest Neighbor (BNN) and so on are available. But they had some difficulties while performing NN in uncertain spatial database. AMNN does not process the queries from distributed server and it accesses the queries only from single server. In SNN, the high dimensional data structure could not be used in NN search and it accesses only low dimensional data for NN search. Approach: The previous works described the process of NN using SNN with marginal object weight ranking. The downside over the previous work is that the performance is poor when compared to another work which performed NN using BNN. To improve the NN search in spatial databases using BNN, we are going to present a new technique as BNN search using marginal object weight ranking. Based on events occurring in the nearest object, BNN starts its search using MOW. The MOW is done by computing the weight of each NN objects and rank each object based on its frequency and distance of NN object for an efficient NN search in spatial databases. Results: Marginal Object Weight (MOW) is introduced to all nearest neighbor object identified using BNN for any relevant query point. It processes the queries from distributed server using MOW. Conclusion: The proposed BNN using MOW framework is experimented with real data sets to show the performance improvement with the previous MOW using SNN in terms of execution time, memory consumption and query result accuracy.
Cited by 0Open access
Huaji Zhu, Jun Chen, Jie Jiang · 2011
ABSTRACT: Nowadays, more and more spatial databases are used in various fields, so the demands for keeping spatial database “fresh ” are growing rapidly. Generally, the end-users get the basic spatial data from professional spatial data producers. There are mainly two kinds of way for transfer the changed data. One is to batch transfer; the other is incremental data transfer. With the batch method, the whole up-to-date database is delivered. This process is time-consuming and might induce significant risks of errors occurrence and information loss.. Recent years, more and more people begin to study incremental updating of spatial database. This method of updating makes it possible to transfer change-only information to the end-users, namely to transfer the incremental data. In order to find out the changed objects in a database, firstly we need to identify uniquely all of the objects. In this paper, we define a geographical object as a new 4-tuple {semantic descriptor, thematic descriptor, spatial descriptor, and temporal descriptor}. Spatial descriptor consists of geometric component, position component and topologic component. What change happens to an object? In order to answer this question, firstly, we have to identity and classify changes of geographical objects. Based on the 4-tuple model of geographical object, we propose taxonomy of the change of geographical object and describe these changes with data/knowledge packets. 1.
Cited by 0
Charles W. Emerson, Sivagurunathan Chinniah, Nina Siu-ngan Lam et al. · 2013
Identifying occurrences of objects of interest in a remotely sensed digital image and finding similar objects in a database of comparable imagery usually involves a high-level semantic description based on visual interpretation of every image in the database. This work proposes a similarity search approach wherein the user identifies an object of interest, spatial and spectral characteristics of the object are calculated, and the result is compared to a database of these same calculations that have been performed on all images in the database. The spatial extent of the object of interest is approximated using a region quadtree decomposition of the image. Spatial indices such as fractal dimension, lacunarity, and Moran’s I index of spatial autocorrelation, along with spectral characteristics expressed as histograms of each band’s gray scale values are matched against a set of these same indices that have been previously calculated for all images in a database. The sum of squared differences between the indices calculated for the quads that form theobject of interest and the same quads in the database yields a ranked list of images that have similar characteristics. The retrieval success rate is highly dependent on the configuration of quads used to define the object of interest and the nature of the object itself. Objects such as a lake shoreline are best retrieved using the gray scale histogram, while urban features that are characterized by their texture are more accurately retrieved using indices such as fractal dimension or lacunarity. 1.
Cited by 0
2008
Modern database applications including computer-aided design, multimedia information systems, medical imaging, molecular biology, or geographical information systems impose new requirements on the effective and efficient management of spatial data. Particular problems arise from the need of high resolutions for large spatial objects. In this short paper, we sketch a new decompositioning approach based on clustering. We propose to describe a voxelized spatial object by a set of Gaussian distribution functions. Based on this decompositioning technique, we propose intersection queries which do not simply return a boolean value for each database object, but assign to each object a probability value indicating how likely an intersection is. The benefit of this approach compared to traditional approaches is that we do not any longer need an expensive refinement step for detecting whether objects intersect exactly on the fine-grained voxel sets. 1.
Cited by 0
Charles W. Emerson, Sivagurunathan Chinniah · 2006
Identifying specific objects in an image and finding similar objects in a database of comparable imagery usually involves a high-level semantic description based on visual interpretation of every image in the database. This work presents preliminary results of a similarity search approach wherein the user identifies an object of interest using a region quadtree decomposition of the image, spatial and spectral characteristics of the object definition are calculated, and the result is compared to a database of these same calculations that have been performed on all images in the database. Spatial indices such as fractal dimension, lacunarity, Moran’s I index of spatial autocorrelation, and wavelet energy signatures, along with spectral characteristics expressed as summary statistics and histograms of a panchromatic image’s gray scale values are matched against a set of these same indices that have been previously calculated for a quadtree decomposition of all images in a database. The sum of squared differences between the indices calculated for the quads that form the object of interest and the same quads in the database yields a ranked list of images that have similar characteristics. In an analysis of an Ikonos panchromatic image of Kalamazoo, Michigan, we found that the retrieval success rate is highly dependent on the nature of the object of interest and the configuration of quads used to define the object.
Cited by 1
Peiquan Jin, Shouhong Wan, Lihua Yue · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
In this paper, a new approach to maintain spatial information acquiring from remote-sensing images is presented, which is based on Object-Relational DBMS. According to this approach, the detected and recognized results of targets are stored and able to be further accessed in an ORDBMS-based spatial database system, and users can access the spatial information using the standard SQL interface. This approach is different from the traditional ArcSDE-based method, because the spatial information management module is totally integrated into the DBMS and becomes one of the core modules in the DBMS. We focus on three issues, namely the general framework for the ORDBMS-based spatial database system, the definitions of the add-in spatial data types and operators, and the process to develop a spatial Datablade on Informix. The results show that the ORDBMS-based spatial database support for image-based target detecting and recognition is easy and practical to be implemented.
Cited by 0
Sang K. Cha, Kihong Kim, Changbin Song et al. · 1997
The need for accessing multiple heterogeneous databases arises frequently in geographic information processing because a single database may not contain all the information in the desired level of abstraction, completeness, and correctness. Two keys to the unification of spatial objects in heterogeneous databases are the ability to connect to multiple databases and manipulate them transparently and consistently, and the ability to resolve various types of semantic heterogeneity among multiple databases. This paper presents an ongoing effort of developing a middle ware for providing transparent and consistent access to multiple spatial object databases. Called Spatial Data Base Connectivity(SDBC), it gives programmers a single, object-oriented view of database and programming with global transaction management and integrity constraint checking. Based on the layered architecture and the extension of the ODMG object model with a set of spatial data types and operators, SDBC provides users...
Cited by 0
Piyavach Khunsongkiet, Jakramate Bootkrajang, Churee Techawut · 2020
Content-Based Image Retrieval (CBIR) is an information retrieval framework for retrieving similar images based on objects in the images. Machine learning based CBIR consists of object detection, the majority of which rely on Convolutional Neural Network (CNN) as object detector, and image similarity ranking. However, object detection with CNN requires expensive retraining when new set of the images is added to the database, while current ranking techniques focus on visual characteristics without considering object's spatial information.
Cited by 1