GDM: A New Graph Based Data Model Using Functional Abstractionx

In this paper, a Graph-based semantic Data Model (GDM) isproposed with the primary objective of bridging the gap between thehuman perception of an enterprise and the needs of computinginfrastructure to organize information in some particular manner forefficient storage and retrieval. The Graph Data Model (GDM) has beenproposed as an alternative data model to combine the advantages of therelational model with the positive features of semantic data models.The proposed GDM offers a structural representation for interacting tothe designer, making it always easy to comprehend the complex relationsamongst basic data items. GDM allows an entire database to be viewedas a Graph (V,E) in a layered organization. Here, a graph is createdin a bottom up fashion where V represents the basic instances of dataor a functionally abstracted module, called primary semantic group(PSG) and secondary semantic group (SSG). An edge in the model impliesthe relationship among the secondary semantic groups. The contents ofthe lowest layer are the semantically grouped data values in the formof primary semantic groups. The SSGs are nothing but the higher-levelabstraction and are created by the method of encapsulation of variousPSGs, SSGs and basic data elements. This encapsulation methodology toprovide a higher-level abstraction continues generating varioussecondary semantic groups until the designer thinks that it issufficient to declare the actual problem domain. GDM, thus, usesstandard abstractions available in a semantic data model with astructural representation in terms of a graph. The operations on thedata model are formalized in the proposed graph algebra. A Graph QueryLanguage (GQL) is also developed, maintaining similarity with thewidely accepted user-friendly SQL. Finally, the paper also presents themethodology to make this GDM compatible with the distributedenvironment, and a corresponding query processing technique fordistributed environment is also suggested for the sake of completeness.