On the Operationalization of Graph Queries with Generalized Discrimination Networks

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 subqu...

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Bibliographic Details
Published inGraph Transformation pp. 170 - 186
Main Authors Beyhl, Thomas, Blouin, Dominique, Giese, Holger, Lambers, Leen
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2016
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN9783319405292
3319405292
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-40530-8_11

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Summary: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 queries. 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.
Bibliography:This work was partially developed in the course of the project Correct Model Transformations II (GI 765/1-2), which is funded by the Deutsche Forschungsgemeinschaft.
ISBN:9783319405292
3319405292
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-40530-8_11