On the Operationalization of Graph Queries with Generalized Discrimination Networks

Abstract

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.

A Logic-Based Incremental Approach to Graph Repair

“…[2,4]), developed with the aim of efficiently re-evaluating a graph query after an update has been performed. Although not employed with the specific aim of complete and least changing graph repair, this work is related to our newly introduced concept of satisfaction trees, also using specific data structures to record with some detail the set of answers to a given query (as described for graph conditions, for example, also in [3]). It is part of ongoing work to evaluate how STs can be employed similarly in this field of incremental query evaluation.…”

Automated reasoning for attributed graph properties

“…Following this idea (cf. [6] and [26]) graphs and graph properties have been used to model graph databases and their queries. In this graph model a graph G (possibly typed over a given type graph T G) models a graph database instance.…”

Symbolic Model Generation for Graph Properties

“…Moreover, we forbid parallel edges of the same type. The first considered graph query (a variant of query 8 from [3]) looks for pairs of Persons and Tags such that in such a pair a Tag is new in some Post by a friend of this Person. To be a Post of a friend, the Post must be from a second Person the Person knows.…”

“…Let us take a closer look at the latter application field to understand how the symbolic model generation approach for graph properties, as presented in this paper, will support a typical usage scenario. In general, a graph query for a graph database G (as formalized in [3] and used in extended form in [18]) formulates the search for occurrences of graph patterns of a specific form L satisfying some additional property in G. Since such a query can become quite complex it is important to have an intuitive query language to formulate it and to have additional support allowing for reasoning about the query to enhance understandability and facilitate debugging. Validity of a graph query means that there should exist a graph database G in which we find an occurrence of the pattern L satisfying the additional property for L encoded in the query, see e.g.…”