Incremental View Maintenance for Deductive Graph Databases Using Generalized Discrimination Networks

Beyhl, Thomas; Giese, Holger

doi:10.4204/eptcs.231.5

Cited by 9 publications

(18 citation statements)

References 24 publications

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“…We plan to study in our future work the complexity of evaluating and updating SGDNs, their optimization, and possible extensions of SGDNs towards path-related properties to also formally cover our own practical work on graph queries [6] supporting cycles in the network.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore the language equivalence L GDN ∼ L NGC only applies unless we leave the realm of pattern-based property specification concepts such as NGC and consider also path-related properties [17] or we permit cycles in the network in a controlled manner as in our own practical work on graph queries [6] to be able to support path-related properties (analogously to the controlled and repeated rule applications to support path-related properties used in [3]). …”

Section: Discussionmentioning

confidence: 99%

“…In our own practical work on graph queries [6], we conceptually employ SGDN with marking rules in form of graph transformation rules for optimization of queries and incremental updates of graph queries. We were able to show that the more powerful capabilities of a single node (marking rule) and advanced dynamic pattern matching strategies [12] can lead to considerable improvements concerning the computation speed and memory consumption for SGDN compared to the restricted case of MSGDN (resp.…”

Section: Discussionmentioning

confidence: 99%

“…However, such a formal framework does not exist for graph queries so far. Consequently, inspired by the relational case we suggest motivated by our practical work on view maintenance for graph databases [6] a simple operational concept for the decomposition of arbitrary complex graph queries into a suitable ordering of simpler subqueries in form of GDNs. Rather than considering one particular kind of GDN with particular network nodes, we suggest employing graph transformation 1.…”

Section: Introductionmentioning

confidence: 99%

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On the Operationalization of Graph Queries with Generalized Discrimination Networks

Beyhl

Blouin

Giese

et al. 2016

Lecture Notes in Computer Science

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Operationalization of Graph Queries with Generalized Discrimination Networks

Beyhl

Blouin

Giese

et al. 2016

Lecture Notes in Computer Science

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“…This thesis uses the deductive inference engine from Beyhl et al [35] for implementing the Deurema analysis framework. The inference engine is an implementation of a model management approach as introduced in the preliminaries in Section 2.2.…”

Section: Discussionmentioning

confidence: 99%

Modeling Collaborations in Adaptive Systems of Systems

Wätzoldt

Giese

2015

Proceedings of the 2015 European Conference on Software Architecture Workshops

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Recently, due to an increasing demand on functionality and flexibility, beforehand isolated systems have become interconnected to gain powerful adaptive System of Systems (SoS) solutions with an overall robust, flexible and emergent behavior. The adaptive SoS comprises a variety of different system types ranging from small embedded to adaptive cyber-physical systems. On the one hand, each system is independent, follows a local strategy and optimizes its behavior to reach its goals. On the other hand, systems must cooperate with each other to enrich the overall functionality to jointly perform on the SoS level reaching global goals, which cannot be satisfied by one system alone. Due to difficulties of local and global behavior optimizations conflicts may arise between systems that have to be solved by the adaptive SoS.This thesis proposes a modeling language that facilitates the description of an adaptive SoS by considering the adaptation capabilities in form of feedback loops as first class entities. Moreover, this thesis adopts the Models@runtime approach to integrate the available knowledge in the systems as runtime models into the modeled adaptation logic. Furthermore, the modeling language focuses on the description of system interactions within the adaptive SoS to reason about individual system functionality and how it emerges via collaborations to an overall joint SoS behavior. Therefore, the modeling language approach enables the specification of local adaptive system behavior, the integration of knowledge in form of runtime models and the joint interactions via collaboration to place the available adaptive behavior in an overall layered, adaptive SoS architecture.Beside the modeling language, this thesis proposes analysis rules to investigate the modeled adaptive SoS, which enables the detection of architectural patterns as well as design flaws and pinpoints to possible system threats. Moreover, a simulation framework is presented, which allows the direct execution of the modeled SoS architecture. Therefore, the analysis rules and the simulation framework can be used to verify the interplay between systems as well as the modeled adaptation effects within the SoS. This thesis realizes the proposed concepts of the modeling language by mapping them to a state of the art standard from the automotive domain and thus, showing their applicability to actual systems. Finally, the modeling language approach is evaluated by remodeling up to date research scenarios from different domains, which demonstrates that the modeling language concepts are powerful enough to cope with a broad range of existing research problems.-I - Zusammenfassung

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A Logic-Based Incremental Approach to Graph Repair

Schneider

Lambers

Orejas

2019

Lecture Notes in Computer Science

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Graph repair, restoring consistency of a graph, plays a prominent role in several areas of computer science and beyond: For example, in model-driven engineering, the abstract syntax of models is usually encoded using graphs. Flexible edit operations temporarily create inconsistent graphs not representing a valid model, thus requiring graph repair. Similarly, in graph databases-managing the storage and manipulation of graph data-updates may cause that a given database does not satisfy some integrity constraints, requiring also graph repair. We present a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of leastchanging repairs. In our context, we formalize consistency by so-called graph conditions being equivalent to first-order logic on graphs. We present two kind of repair algorithms: State-based repair restores consistency independent of the graph update history, whereas delta-based (or incremental) repair takes this history explicitly into account. Technically, our algorithms rely on an existing model generation algorithm for graph conditions implemented in AutoGraph. Moreover, the delta-based approach uses the new concept of satisfaction (ST) trees for encoding if and how a graph satisfies a graph condition. We then demonstrate how to manipulate these STs incrementally with respect to a graph update.

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Incremental View Maintenance for Deductive Graph Databases Using Generalized Discrimination Networks

Cited by 9 publications

References 24 publications

On the Operationalization of Graph Queries with Generalized Discrimination Networks

On the Operationalization of Graph Queries with Generalized Discrimination Networks

Modeling Collaborations in Adaptive Systems of Systems

A Logic-Based Incremental Approach to Graph Repair

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