A Novel Method for the Comparison of Graphical Data Models (original) (raw)
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A Data Model is the foundation of any software application. These applications can be Retail, Accounting, Customer Relationship Management, Finance, Analysis, Decision Support, Transaction Processing or Analysis Processing to name just a few. These data models are purpose built following best practices of normalization as documented by Codd and Date. Comparison of two Data Models can become subjective since there are limited quantifiable metrics to do a comparison. Extensive research has been done exploring methods for the comparison of Graphs. Here we will present a mechanism for translating a Data Model into a Data Structure Graph that can have the best practices of Graph Theory applied to it, as well as use the metrics that are provided by Graph Theory for quantification and comparison of Data Models.
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Nowadays approaches, based on models, are used in the development of the information systems. The models can be changed during the system development process by developers. They can be transformed automatically: visual model can be translated into program code; transformation from one modeling language to other can be done. The most appropriate way of the formal visual model presentation is metagraph. The best way to describe changes of visual models is the approach, based on graph grammars (graph rewriting). It is the most demonstrative way to present the transformation. But applying the graph grammar to the graph of model means to find the subgraph isomorphic to the left part of the grammar rule. This is an NP-complete task. There are some algorithms, developed for solving this task. They were designed for ordinary graphs and hypergraphs. In this article we consider some of them in case of using with the metagraphs representing models.
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In intelligent knowledge-based systems, the task of approximate matching of knowledge elements has crucial importance. We present the algorithm of comparison of knowledge elements represented with conceptual graphs. The method is based on well -known strategies of text comparison, such as Dice coefficient, with new elements introduced due to the bipartite nature of the conceptual graphs. Examples of comparison of two pieces of knowledge are presented. The method can be used in both semantic processing in natural language interfaces and for reasoning with approximate associations.
Proc. of 2nd International Workshop on Graph and Model Transformation 2006
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Nowadays, many application data models are developed and maintained based on the so-called ‘Model Driven Architecture (MDA)’ in the fields of software and systems engineering. In general, the MDA stipulates that the data model shall be first defined as an abstract platformindependent model (PIM) and later automatically translated into different platform-specific models (PSM) depending on the target application environments (cf. GASEVIC et al. 2006). A typical case of applying model transformation in real-world applications is the automatic derivation of a relational database schema from an object-oriented data model for realizing the efficient management of large and complex-structured data by making full use of the capabilities of the relational database management systems (RDBMS). According to the literature (cf. NG & LEARMONT 2002), the general conceptual solution is to map the source and target data models onto computer-interpretable formats, such that the model transformation p...
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Conceptual graphs allow for powerful and computationally affordable representation of the semantic contents of natural language texts. We propose a method of comparison (approximate matching) of conceptual graphs. The method takes into account synonymy and subtype/supertype relationships between the concepts and relations used in the conceptual graphs, thus allowing for greater flexibility of approximate matching. The method also allows the user to choose the desirable aspect of similarity in the cases when the two graphs can be generalized in different ways. The algorithm and examples of its application are presented. The results are potentially useful in a range of tasks requiring approximate semantic or another structural matching -among them, information retrieval and text mining.
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