Dual deep modeling: multi-level modeling with dual potencies and its formalization in F-Logic

Abstract

An enterprise database contains a global, integrated, and consistent representation of a company's data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities.Communicated by Prof.

DeepTelos: Multi-level Modeling with Most General Instances

“…DeepTelos is also related to our work on Dual Deep Instantiation [15,16]. In DDI, a class together with its MGI chain would be represented by a single clabject where each property has source and target potency.…”

“…The source code includes in comments the potencies of clabject, attributes and relations. These comments serve for comparing DeepTelos with dual deep instantiation (DDI) [15,16], see discussion in Section 5. …”

“…Early approaches were materialization [19] and power types [18]. Many current approaches [6,20,12,16] to multi-level modeling assign potencies to relationships, attributes, and classes. Potencies constrain how many times the respective concept can be instantiated until it can no further be instantiated.…”

Join potency

“…Each of the aforementioned potency notions has been already implemented, for example, classic potency was integrated into DeepJava [12], leap potency is an essential part of MetaDepth [7], and star potency is supported by both MetaDepth and Melanee [1]. Although there are other multi-level techniques which are frequently used [6,17], regarding the contributions of this paper, the most relevant ones are the leap and the star potency.…”

“…They do not support n-ary connections and they cannot impose constraints on the cross-level references how they are to be refined later by instantiation. The dual deep modeling approach [17] solves some of these issues by defining deep properties which have source and target potencies which can be asymmetrical. However, refinable constraints and n-ary connections are not supported by dual deep modeling yet.…”