Graph queries have lately gained increased interest due to application areas such as social networks, biological networks, or model queries. For the relational database case the relational algebra and generalized discrimination networks have been studied to find appropriate decompositions into subqueries and ordering of these subqueries for query evaluation or incremental updates of query results. For graph database queries however there is no formal underpinning yet that allows us to find such suitable operationalizations. Consequently, we suggest a simple operational concept for the decomposition of arbitrary complex queries into simpler subqueries and the ordering of these subqueries in form of generalized discrimination networks for graph queries inspired by the relational case. The approach employs graph transformation rules for the nodes of the network and thus we can employ the underlying theory. We further show that the proposed generalized discrimination networks have the same expressive power as nested graph conditions.
The notion of a programming paradigm is used to classify programming languages and their accompanying workflows based on their salient features. Similarly, the notion of a modelling paradigm can be used to characterise the plethora of modelling approaches used to engineer complex Cyber-Physical Systems (CPS). Modelling paradigms encompass formalisms, abstractions, workflows and supporting tool(chain)s. A precise definition of this modelling paradigm notion is lacking however. Such a definition will increase insight, will allow for formal reasoning about the consistency of modelling frameworks and may serve as the basis for the construction of new modelling, simulation, verification, synthesis, . . . environments to support design of CPS. We present a formal framework aimed at capturing the notion of modelling paradigm, as a first step towards a comprehensive formalisation of multi-paradigm modelling. Our formalisation is illustrated by CookieCAD, a simple Computer-Aided Design paradigm used in the development of cookie stencils.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.