Combining 3D Simulation Technology With Object-Oriented Databases: A Database Oriented Approach to Virtual Reality Systems

Author(s):  
Martin Hoppen ◽  
Juergen Rossmann ◽  
Michael Schluse ◽  
Ralf Waspe ◽  
Malte Rast

Using object-oriented databases as the primary data source in VR applications has a variety of advantages, but requires the development of new techniques concerning data modeling, data handling and data transfer from a Virtual Reality system’s point of view. The many advantages are outlined in the first part of this paper. We first introduce versioning and collaboration techniques as our main motivation. These can also be used in the traditional file based approach, but are much more powerful when realized with a database on an object and attribute level. Using an object-oriented approach to data modeling, objects of the real world can be modeled more intuitively by defining appropriate classes with their relevant attributes. Furthermore, databases can function as central communication hubs for consistent multi user interaction. Besides, the use of databases with open interface standards allows to easily cooperate with other applications such as modeling tools and other data generators. The second part of this paper focuses on our approach to seamlessly integrate such databases in Virtual Reality systems. For this we developed an object-oriented internal graph database and linked it to object-oriented external databases for central storage and collaboration. Object classes defined by XML data schemata allow to easily integrate new data models in VR applications at run-time. A fully transparent database layer in the simulation system makes it easy to interchange the external database. We present the basic structure of our simulation graph database, as well as the mechanisms which are used to transparently map data and meta-data from the external database to the simulation database. To show the validity and flexibility of our approach selected applications realized with our simulation system so far e. g. applications based on geoinformation databases such as forest inventory systems and city models, applications in the field of distributed control and simulation of assembly lines or database-driven virtual testbeds applications for automatic map generation in planetary landing missions are introduced.

Author(s):  
Terry Halpin

The Unified Modeling Language (UML) was adopted by the Object Management Group (OMG) in 1997 as a language for object-oriented (OO) analysis and design. After several minor revisions, a major overhaul resulted in UML version 2.0 (OMG, 2003), and the language is still being refined. Although suitable for object-oriented code design, UML is less suitable for information analysis, since its graphical language provides only weak support for the kinds of business rules found in data-intensive applications, and its textual Object Constraint Language (OCL) is too technical for most business people to understand. Moreover, UML’s graphical language does not lend itself readily to verbalization and multiple instantiation for validating data models with domain experts. These problems can be remedied by using a fact-oriented approach for information analysis, where communication takes place in simple sentences, each sentence type can easily be populated with multiple instances, and attributes are avoided in the base model. At design time, a fact-oriented model can be used to derive a UML class model or a logical database model. Object Role Modeling (ORM), the main exemplar of the fact-oriented approach, originated in Europe in the mid-1970s (Falkenberg, 1976), and has been extensively revised and extended since, along with commercial tool support (e.g., Halpin, Evans, Hallock, & MacLean, 2003). Recently, a major upgrade to the methodology resulted in ORM 2, a second-generation ORM (Halpin 2005). Neumont ORM Architect (NORMA), an open source tool accessible online at http://sourceforge.net/projects/orm, is under development to provide deep support for ORM 2 (Curland & Halpin, 2007). This article provides a concise comparison of the data modeling features within UML and ORM. The next section provides background on both approaches. The following section summarizes the main structural differences between the two approaches, and outlines some benefits of ORM’s factoriented approach. A simple example is then used to highlight the need to supplement UML’s class modeling notation with additional constraints, especially those underpinning natural identification schemes. Future trends are then briefly outlined, and the conclusion motivates the use of both approaches in concert to provide a richer data modeling experience, and provides references for further reading.


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