this paper we report upon a graph-based approach to such an integration. Our use of graphs has two key advantages : firstly, graphs are formally defined, well-understood structures; secondly, it is widely accepted that graph-based formalisms considerably enhance the usability of complex systems [19]. Graphs have been used in conjunction with a number of conventional data models, for example the hierarchical and network models [35], the entity-relationship model [9] and a recent extension thereof for complex objects [27], and various semantic data models [16, 20, 31]. Graphs or hypergraphs [6] have also been used more recently in [12, 17, 23, 25, 33, 36] as a data modelling tool in their own right. We give a comparison between this recent work and our own approach in Section 4 of the paper. Directed graphs have also been the foundation of Hypertext databases [11, 33]. Such databases are graphs consisting of nodes which refer to units of stored information (typically text) and of named links. Each link connects two nodes, the "source" and the "destination". Links are traversed either forwards (from source to destination) or backwards (from destination to source). The process of traversing named links and examining the text associated with nodes is called

Graph database models can be characterized as those where data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models flourished in the eighties and early nineties in parallel to object oriented models and their influence gradually faded with the emergence of other database models, particularly the geographical, spatial, semistructured and XML. Recently, the need to manage information with inherent graph-like nature has brought back the relevance of the area. In fact, a whole new wave of applications for graph databases emerged with the development of huge networks (e.g. Web, geographical systems, transportation, telephones), and families of networks generated due to the automation of the process of data gathering (e.g. social and biological networks). The main objective of this survey is to present in a single place the work that has been done in the area of graph database modeling, concentrating in data structures, query languages and integrity constraints.

Modern software systems for application areas like software engineering, CAD, or office automation are usually highly interactive and deal with rather complex object structures. For the realization of these systems a nonstandard database system is needed which is able to efficiently handle different types of coarse- and fine-grained objects (like documents and paragraphs), hierarchical and non-hierarchical relations between objects (like composition-links and cross-references), and finally attributes of rather different size (like chapter numbers and bitmaps). Furthermore, this database system should support incremental computation of derived data, undo/redo of data modifications, error recovery from system crashes, and version control mechanisms. In this paper, we describe the underlying data model and the functionality of GRAS, a database system which has been designed according to the requirements mentioned above. Furthermore, we motivate our central design decisions concerning its ...

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