VGEs-Oriented Multi-sourced Heterogeneous Spatial ...

VGEs-Oriented Multi-sourced Heterogeneous Spatial Data Integration Hongjun Su*, Yongning Wen, Min Chen, Hong Tao,Jingwei Shen Key Laboratory of Virtual geographic environments (Ministry of Education), Nanjing Normal University, Nanjing, Jiangsu Province 210046, P. R. China ABSTRACT The world of today heavily relies on spatial data to manage the natural and built environments, also to construct virtual geographic environments (VGEs). After analyzed the characteristics of multi-sourced heterogeneous spatial data in VGEs, a Data Representation Model oriented to VGEs (XGE-DRM) was designed in this paper. The XGE-DRM provides not only a clear description of the data, but also defines the relationships among the data, it is critical for users to interpret data correctly. XGE-DRM provides a common data model to define a data representation structure for traditional data and spatial data. Using it, users can customize their geographic data to correctly interpret other data. Then the processes and framework of multi-sourced heterogeneous spatial data integration were proposed, and the spatial data parsing was discussed. Lastly, the platform system of multi-sourced heterogeneous spatial data integration was designed and developed under the VS.NET2005 development environment combined with XML technology, and it can solve the bottleneck problems arise in geographic information resources which under the heterogeneous environment. Some experiments were designed and discussed, by those experiments; it had shown that the platform we developed can integrate multi-sourced heterogeneous spatial data effectively and were works better than other similar platform systems. Keywords: multi-sourced heterogeneous spatial data; data integration; XGE-DRM; virtual geographic environments

1. INTRODUCTION The development of GIS is covered of desktop GIS, network GIS, virtual GIS and virtual geographic environments (VGEs). VGEs, which is the opposite of the real environment, is the reflection of the real environment in mental scene, and is the concentrate and representation of the real environment. As we all know that, the data from several fields is needed to construct virtual geographic scenario, but most of the data used in VGEs are heterogeneous and distributed in different places. It is pity that the diverse understanding to spatial objects in cognition, field’s requirement, data collection methods and data modelling caused the heterogeneous structure in spatial data model, database, and access interface and so on. And all of those are handicapped data integration, sharing and interchange badly. At the same time, XML is becoming the standard of data representation, integration and interchange, and it is an effective method to integrate heterogeneous data. It has the prior characteristics of self-description, structured and platform independence, and can describe different sourced data neatly. In fact, data integration and sharing technology based on XML is the activated study fields in GIS and computer science. And the characteristics of multi-source and heterogeneity for spatial data delayed the construction of VGEs badly. It is urgent to find a new solution namely a new data integration strategy for understanding, describing and representing geographic data perfectly in order to integrate multi-sourced heterogeneous data in VGEs. So it is useful and meaningful to research spatial data integration. In order to integrate the multi-sourced heterogeneous data, the characteristics of multi-sourced heterogeneous data are analyzed; some new ideas and methods for understanding, describing and representing geographic data profoundly are discussed in this paper. In detail, the Data Representation Model oriented to Virtual Geographic Environments (XGEDRM) is proposed and designed based on XML; the processes and framework of multi-sourced heterogeneous data integration are discussed; a prototype of multi-sourced heterogeneous data integration system is developed. At last, some experiments are implemented in detailed to examine the XGE-DRM proposed in the paper.

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Corresponding author: Email [email protected], [email protected], Phone: +86-13851706937 Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, edited by Lin Liu, Xia Li, Kai Liu, Xinchang Zhang, Proc. of SPIE Vol. 7146, 71460D · © 2008 SPIE CCC code: 0277-786X/08/$18 · doi: 10.1117/12.813104 Proc. of SPIE Vol. 7146 71460D-1

2. STATE-OF-THE-ARTS OF VGES AND SPATIAL DATA INTEGRATION 2.1 VGEs Virtual Geographic Environments has developed quickly after it was proposed before 10a ago. The scholars especially the Chinese scholars have done much research works in this field. H. LIN and J.H. GONG[1-5] et al. addressed the conception and characteristics of VGEs for the first time around world, and have done more profoundly works lately, such as theory ,methodology ,technology and applications of VGEs; for the most important they thought that the VGEs was not just only a framework which emphasizing data sharing in GIS, it has became a knowledge sharing platform which emphasizing database and model database, that means it can create new knowledge based on itself. LU Guonian and WEN Yongning[6-9]et al. have done the work mostly in the follow fields: geographic cognition theory in virtual space, 3D spatial data acquisition and integrated data model, geographic analyzing model and integration between it and VGEs, distributed geographic modelling environment etc. What’s more, the framework of VGEs system was designed, also the overall design and detail design of VGEs system were discussed and their works is useful for the development of VGEs system platform from the view of software. LI Shuang[10]et al. discussed the geographic representation model especially face representation model, voxel representation model and interchange, modelling of the multi-dimensional data model; and it has recognized that the researches on VGEs in the last years results in the significant influence on the thoughtway of geography and earth system science. In addition, there are some works on the spatial data model around the world, some useful models were proposed. Unfortunately, most of them are focusing on the modelling of the geoentity itself, still pay little attention to the integration of the multi-sourced heterogeneous spatial data and its integration model even oriented to VGEs, and it is a pity that it is not helpful for the development of the geography. In a word, the researches on the VGEs mostly limited in the follow fields: the recognition, conception and framework of the VGEs; system designing, software development; virtual city and 3D visualization representation models oriented to applications, etc. For the most important, the following issues are still not solved when GIS as the data support environment for VGEs: Firstly, the existed data models in different GIS systems do not support geo-data sharing and interoperability because the discrepancy of understanding, description and representation for spatial data. Secondly, the characteristics of heterogeneous and special application fields weaken the support for geographic model analysis based on geo-mechanism and process, also VGEs construction. Lastly and for the most important, the VGEs based on Internet with little interesting in heterogeneous spatial data results in the useless of the geographic environment sources which distributed in heterogeneous environments. In fact, VGEs, as the opposite of the real environments, is the reflection of really environment in mental scene. The defined conception for this new term is follows defined by LU Guonian in 2002: VGEs is a virtual environment system in computer and cyberspace, in this 3D space environment created by computer, geographer can understand and get the feeling of spatio-temporal relationships between physical features and humanism features, analyze geographic problems using quantitative analysis, numerical simulation methods and interactive collaborative technology, represent the geographic environment visually, simulate the geographic phenomenon, reproduce and predict the changes of geographic environment, and explore geographic law in the nature. As a research platform for earth system science, VGEs is oriented to geographic study and analysis of multi-domains; and it is very meaningful tool for geographic modelling and simulation and geographic problem solving. However, the studies on the geographic analysis and geographic modelling still in the exploratory stage, only a few touch upon the integration of multi-sourced heterogeneous data. It is urgent to start the researches on multi-sourced heterogeneous data integration oriented to VGEs for the development of VGEs 2.2 Multi-sourced heterogeneous data integration In the last years, the spatial data integration and sharing have been investigated by the researchers, and some achievements have reached. Robert M. Bruckner [11] et al. proposed a framework using XML Topic Maps (XTM) as a foundation for the combination of Web OLAP and data warehouse resources by integrating schema information and addressing semantic heterogeneity; and providing, managing and exploiting a set of integrated data warehouses for knowledge management and decision support. R. Fileto[12]analyzed the problems of heterogeneous spatial data integration, discussed the characteristics of heterogeneous data, designed the integration process of heterogeneous data, and presented several facets of the data integration problem, and some approaches to deal with them. Koen Aerts[13]et al.

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described the development of an Ontology-Driven Geographic Information System for the integration of vector-based, large-scale topographic data, attempted to solve semantic heterogeneity of topographic feature classes using new semantic web technologies, such as the Web Ontology Language; also demonstrated in a practical example of road networks in topographic databases. In order to support the sharing and integration of geo-spatial information, building information and other related information, Hongxia Wang [14] designed a Web-based information service framework, based on a loosely-coupled mediator system and Open Geospatial Consortium’s Web Services (OWS) architecture. Mohammadi, H[15] et al. thought that the diversity in data providers creates a great deal of inconsistency in the integration of the datasets, and it including institutional, technical, social, legal and policy heterogeneity, and for the most important that these heterogeneities hinder different aspects and components of a spatial society to facilitate data flow, access and integration. K.A. Karasavvas[16] discussed the problematic issues in Bio-informaticians data integration, and introduce agent technology into this field. Different investigations of data integration have proposed different solutions and examples in respective fields. Unfortunately, most researches are localizing in the follows three categories, namely data format interchange, data access directly, spatial data standard Interchange etc., and still belongs to the conventional methods in multi-sourced heterogeneous spatial data integration. All of those methods have their disadvantage in processing heterogeneous spatial data. The problems occurred should be paid more attention to. Firstly, the differentia of data models vary with understanding of spatial data in different GIS software hampered the development of spatial data integration and interchange. Secondly, take all heterogeneous data into one format disobey the principle of distribution and independence for spatial data. Also, in order to integrate multi-format data, the data access interface should be a common format used widely; it is not realistic in the near future. So, all the above three methods may have difficulty to integrate heterogeneous spatial data. Data integration provides the ability to manipulate (e.g., query, analysis and visualization) data transparently across multiple heterogeneous data sources [17]. In order to solving integration of multi-sourced heterogeneous data, three problems should be taken into account (LU Guonian, 2005): data format exchange, geographic symbols sharing and topology reconstruction. Of them, topology reconstruction is the most significant issues because it is the base of simulation, reproduction of the geographic entities and phenomenon, also is the key characteristics which can distinguish geo-data integration from others. In order to hurdle those fencings, a new data model which can conceal the heterogeneous in system environment and inner data structure should be designed to integrate multi-sourced heterogeneous data seamlessly.

3. CHARACTERISTICS OF MULTI-SOURCED HETEROGENEOUS SPATIAL DATA Spatial data is the key part in the traditional GIS systems. At present, the development of GIS technology is transferring from land/landscape oriented GIS to human oriented GIS, and spreading from earth-system–science-oriented GIS to socialized and public GIS; and the requirement for the spatial data is desiderated. But, in fact, most of the spatial data are heterogeneous and located in different places around the world, and making the difficulties in using those spatial data. Heterogeneous data integration is a very general problem, but it can be disassemble in some interrelated sub-problems, making it more tractable. In the following, the major categories of data heterogeneity are discussed in order to establishing a basis for discussions throughout this paper. 3.1 Multi-source Multi-source mainly means multi-sourced in data acquiring, storing format, semantic, spatio-temporal and scales. It is roots in the following aspects: Multi-sourced: the rapid development of geographic information science largely expanded data collection methods for us; the data collection methods commonly used include digital map, measured data from fields, experiment data, also RS, GPS, integrated data etc.. Multi-producer: data formats varying with data produced departments due to different understanding in different fields. Multi-data-model: the diverse understanding to spatial objects in cognition, representation, organization and storing caused the heterogeneous structure in spatial data model [18].

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Multi-representation and visualization: owing to lacking of data integration and sharing, data representation and visualization is dependent on data model and format badly. 3.2 Heterogeneity Heterogeneous data are those data which presenting differences in their representation or interpretation, although referring to the same reality [19]. Heterogeneity in spatial data can be divided into some types, namely data values, schema, data model, syntactic and semantic etc [12, 20-21]. Data value heterogeneous related to the representation or the interpretation of the data values. Examples of this type heterogeneity is discrepancies of type, unit, precision, allowed values (i.e. enumerated values for user defined atomic types), and spelling (abbreviations, typing mistakes, etc.). Schematic heterogeneity can be defined as different aggregation or generalization hierarchies for the same “real world” facts. For example, documents can contain the same element and attribute names but have different nested structures. Data model heterogeneity means databases use different data models, this is reflected on database schemas; e.g., one database designed according to the relational model, and another one object-oriented. Syntactic heterogeneity refers to different paradigms that are discrepancies in the representation of data, e.g. XML, Relational and RDF. Semantic heterogeneity refers to disagreement on the meaning, interpretation or intended use of data, for example, documents can have the same names for elements and attributes but different meanings. In addition, the heterogeneity of spatial data can be categorized into two levels take into account additional discrepancies that can arise between heterogeneous schemas and different data models. One is abstraction level and the other is representation and interpretation level. In which, data values heterogeneity, schema heterogeneity, data model heterogeneity belongs to abstraction level; while representation and interpretation level is consist of syntactic heterogeneity and semantic heterogeneity (shown in Figure 1). For the most important, semantic data integration can be used to solve other heterogeneities.

Figure.1 Characteristic heterogeneous of spatial data

4. DATA REPRESENTATION MODEL ORIENTED TO VGES 4.1 Comprehensive VGEs In present, the development of VGEs has spread into the stage of “human-centered”. In fact, the real worlds are composed of a series of “aspects” in people’s eyes, and the VGEs varying with the understanding of aspect for different geographic phenomenon. And we thought that the VGEs are a comprehensive geographic environment which consisting of multi-aspects for real scenario. Geographic environment has the characteristics of hierarchy, inherent relationships among geo-entities, and it is a whole entity. The real worlds are composed by a series of small entities, and those small entities can build up more complex entities. The entity which can not be divided up is named atom-entity, and it is the minimum cell that composes the complex entity and systems, and each of them has the specific data type. Based on the analysis above, Data Representation Model oriented to VGEs (XGE-DRM) is designed; it is an object-oriented data representation tool.

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4.2 Data Representation Model XGE-DRM, an object-oriented data representation model, based on XML technology, is the key part of the framework of multi-sourced heterogeneous data integration, and it is the foundation of extensible geography environment modelling and integration platform (XGE). A data representation model is a description used to provide identification of all data elements within a system, including their attributes and the logical relationships among data elements. In object oriented terminology, this is viewed as a class hierarchy, and described through a graphics-based design tool. The XGE-DRM provides not only a clear description of the data, but also defines the relationships among the data, it is critical for users to interpret data correctly. XGE-DRM provides a common data model to define a data representation structure for traditional data and spatial data. Using it, user can design their geographic data structure in order to correctly interpret other data. Some namable projects such as SEDRIS [22], Ptolemy [23] and ArcGIS [24] were referenced when design XGE-DRM. The priority of it to other data models is that it can provides an effective data describing and representation method namely geographic data representation model, and also provides a data exchange mechanism for distributed geographic data. There are also two key principles when design XGE-DRM, one is separating the semantics of what something represents from the “data primitives” used to represent it, the other is factoring out the common syntax and semantics of data models used to represent similar objects. Figure 2 has shown the interfaces of XGE-DRM. XGE-DRM contains several parts such as Prototypes, Tokens, Shapes, Spatial Mesh and so on, and they can describe the real environment after be combined together. In the Tokens parts, there are some data types in common use as follows: Int8,Uint8,Int16,…,Double,Float,DateTime,String,Point,Line,Polygon,Zware etc. The geograhpic entities can be described by those data types, and in particularly, those data types are enclosed in Prototypes container as prototypes can be modified when designing.

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► Prototypes: Dealing with single data type, and providing root prototype for all kinds of data types. ► Tokens: Dealing with complex data type such as value, list, array, table and so on, which is composed by single data type in Prototypes. ► Shapes: Representing geometric data types which mainly include Point, Multi-Point, Line, Multi-Line, Polygon and complex polygon etc. ► Spatial Mesh: Describing the structure of spatial partition such as Tin, Grid, and Ten and so on. ► Locations: Tracking the original location of data.

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► Associations: Storing the map or relation between different data, or topology. ► Semantic: Specification and interpretation of the data, and providing a categorization mechanism for geographic object. This work is partly referenced to ECDS of SEDRIS [22]. ► Unit&SRS: Describing the unit and spatial reference of data, in which Unit was referenced the Unit system of Ptolemy [23], and SRS was referenced SRS of ArcGIS & SEDRIS [22, 24]. ► Meta: Data about data. It is should be paid more attention to that, data and models are all heterogeneous, also application fields are scattered. So, in order to solve geographic problems, the interchanges among them or themselves are needed. For the most important, the XGE-DRM could support multi-domain applications and could satisfy the requirement of VGEs which oriented to multi-domain. The demerit of XGE-DRM is that it only designed as a data organization pattern in our research now and still can not used as a common data model for data integration. 4.3 Multi-sourced heterogeneous data integration process In this paper, the process of multi-sourced heterogeneous data integration based on XGE-DRM is designed and it mainly includes: normalize prototype template, design data template, construct data container, create instance etc. Firstly, the basic data types consist of prototype template. The prototype template is stored in PrototypeLib as XML format, and the prototype in PrototypeLib is abstractly and can not be used directly until assigned name and ID information and stored in XML format. Single prototype template could be converted to complex prototype template by methods of combination and nested. The prototype stored in PrototypeLib can be edited, add semantic information and spatial references etc. according to the use’s requirement.

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Secondly, Data Template is provided to describe the structural information and semantics attached for the heterogeneous data. The structural information refers to the organization and management for the spatial data, and the semantics attached to structural information describing the meanings of the structure and data. The process of design data template includes format interchange, semantic matching, data fusion, pattern extraction, metadata interpretation etc. It is should be paid attention to is that only structural information of data are stored, and the data template can not store the data itself. Thirdly, Data Container is designed to store data template and provide the interface for input data. The structural information of data can be extracted from heterogeneous data sources and put into data container, and it is the structural relationship that becomes the bridge between data template and data container.

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Lastly, the spatial data and its semantics put into data container based on prototype template and data template and when it finished the instance are created. The VGEs are composed of a series of instances.

5. THE FRAMEWORK OF MULTI-SOURCED HETEROGENEOUS DATA INTEGRATION ORIENTED VGES In this paper, the research on multi-sourced heterogeneous data integration framework and XGE-DRM is the foundation of extensible geography environment modelling and integration platform. Under this integration platform, the multisourced, heterogeneous, semantics diversely and distributed data can be reused and shared; and the different geographic data can be parsed, integrated and geographic environments can be represented. The platform can provide a problem solving environment for geographic problem solving. The framework of multi-sourced heterogeneous spatial data consists of the following subparts: geographic data parsing, interaction between geo-data and geographic model, and geographic model parsing, running and results output and so on. In detail, geographic data parse and interaction between geo-data and geographic model are prominent discussed. Figure 4 has shown the framework of multi-sourced heterogeneous data integration oriented to VGEs. 5.1 Multi-sourced heterogeneous geographic data parsing Semantics interchange, as the primary task of data parsing, can provide a spatial data engine to extract different aspects and contents from the same data source by users for data input and output. The spatial data engine provides the methods for bi-interchange among models and redefined the data model.

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Geo-symbol sharing is the problem to be solved in 3D representation. The inner structures of different data storing formats are different, but they could be represented in common way. A common symbol library can be shared for the multi-sourced heterogeneous data when construct virtual scenario, and the symbols which matched with the data will be selected for 3D visualization.

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Topology reconstruction is the last stage for data parsing. In the traditional data interchange, some topologies would be lost; so when processing the heterogeneous data, the topology should be taken into account for reserving the original data information. In XGE-DRM, the topology is mapped and described by Associations. 5.2 Geographic model parsing The purpose of model parsing is enabling the interchange between data and models. The mainly function of model parsing is extracting the requirement of model, and represent the data format and semantics which model required. The decomposing of geo-model not only includes analyzed on code resources and structure, but extracted sub-geo-model, coupling and nested rules of geo-model. In detail, the parsed data format, data semantic and coupling rules, nested rules, parameter extraction rules of geo-model, will be described by XML. Then, searching the prototypes which existed in PrototypeLib, if the search results can match the data template, the geo-model parsing process is end; otherwise, create a new prototype template. 5.3 Interactions between geo-data and geographic models A geographic model has relationships with many data sources, and geo-model has close relations with spatial data though they are independent in logic. The interaction between model and spatial data is the crucial problems should be paid more attention to. The interaction between data and model refers to three meanings: heterogeneous data, data interchange specification, model data. Spatial data parsing has been discussed in section 5.1; data interchange specification namely XGE-DRM this paper proposed, is the bridge for interaction between heterogeneous data and model, and model-data means data requirements(input/output parameters and control parameters) derived from the models. Interactions between geo-data and geographic model are shown in Figure 5. DataEntity

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Firstly, the requirement of geographic conceptual model for geo-model and data were clearly list by analyzing the relations among geographic conceptions in the view of geographic conceptual model level. At the same time, from the view of XGE-DRM, the prototype templates were created based on conceptual models and prototypes.

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Then, geographic compute model types and data types or parameters it needed were searched by associate relationships of conceptions based on the analysis on metadata of categorization system, scope of application, spatio-temporal scales, model quality, parameter types, running conditions and so on. In XGE-DRM, complex prototypes were created by combined and nested simple prototypes according to the demands, and this is an iterative process. Lastly, geographic compute models were configured based on model solving methods and integration pattern etc. the coupling of models in the view of data were achieved, data transfer and function revoking was implement by message delivery methods. In XGE-DRM, data prototypes combined with prototype template were constructed, and were put into data container. When data were inserting, the geo-model would be executed. 5.4 Geographic model execution After the steps above, the geo-model can be executed. For the simple models, the geographic problems could resolve based on the steps above, but for the complex geo-models, maybe some new data were needed in the process of model execution. If then, model parsing and interaction between data and model would be executed again. Also, the interaction between different complex geo-models should emphasize for complex geographic problems.

6. EXPERIMENTS AND DISCUSSIONS The platform system of multi-sourced heterogeneous spatial data integration is designed and developed under the VS.NET2005 development environment combined with XML technology and spatial data integration technology, and it can solve the bottleneck problems arise in geographic information resources which under the heterogeneous environment. Some experiments are designed and discussed, by the experiment, it has shown that the platform we developed can integrate multi-source heterogeneous spatial data effectively and works better than other similar platform systems. Figure 6 has shown multi-sourced heterogeneous data parsing and Figure 7 has shown the results for two geomodel integration experiments.

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In addition, the work discussed above is a part of the ongoing project; more works will be done in the next days. A largely virtual geographic scenario will be designed and constructed based on the XGE-DRM in the future for multisourced heterogeneous distributed data integration. It is helpful for advanced geography researches.

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ACKNOWLEDGEMENT The authors thank professor LU Guonian for his instructive advices; also thank the grant from Key Program of Natural Science Foundation of China (No. 40730527).

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