An efficient implementation of graph grammars based on the RETE matching algorithm

Bunke, Horst; Glauser, Thomas; Tran, Thanh Ha

doi:10.1007/bfb0017389

Cited by 25 publications

(18 citation statements)

References 9 publications

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“…[11]) such as [7] and VI-ATRA [4] that can be conceptually mapped to MSGDN. In these cases the RETE network structure supports only at most two direct dependencies like MSGDN and the computations of the nodes of the RETE network can be matched to the four permitted cases of MSGDN.…”

Section: Discussionmentioning

confidence: 99%

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%

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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“…The Rete algorithm was originally created by Charles Forgy for rule-based expert systems [9]. Bunke et al [6] were the first to propose the Rete algorithm in the context of graph transformations.…”

Section: Incremental Approachesmentioning

confidence: 99%

Evaluation of Optimization Strategies for Incremental Graph Queries

Szárnyas

Maginecz²,

Varró

2017

Period. Polytech. Elec. Eng. Comp. Sci.

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Keywords graph queries, relational algebra, query optimization IntroductionThe key components of Big Data are often defined as variety, velocity and volume [28] of data. Applications operating on continuously changing graphs are a prime example: the semi-structured graph-like nature introduces a high variety, changes happen at high velocity, and datasets are often high-volume. Such applications include fraud detection in financial transactions [27], validation of engineering models [3], and static analysis of source code repositories [35]. These use cases provide a set of complex queries that need to be evaluated continuously on each change of the underlying graph.Traditional approaches need to reevaluate each query upon each change, which often takes minutes on a large dataset. In contrast, incremental query evaluation caches interim results, hence it only requires reevaluation on a small fragment of the dataset impacted by the change. This leads to significant speedup for large and continuously changing data. Although several approaches exist for incremental query evaluation [9,20] in the context of expert systems, incremental query evaluation is not in widespread use in graph databases.In order to predict query performance at runtime, relational databases synthesize and evaluate different query plans which impose a certain ordering on relational algebraic operations prescribed by the query. Optimizing query plans is a challenging task, since a wide variety of query plans may exist even for simple queries with different costs. Database engines use heuristics-based optimization techniques and evaluate a cost function for the different query plans [10].Query plans have been adapted for graph query engines using a local-search based query evaluation strategy where it is called the search plan. Optimization techniques may exploit the type and multiplicity information defined in the graph schema (or metamodel) [29,22] or rely upon runtime statistics of the instance graph [11,38,39].In case of incremental graph query engines, the structure and the content of caches have the most significant impact on query performance. Therefore, optimization is directed to reduce execution time and memory consumption imposed by a complex network of caches [37].

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“…The authors of [8] adapted the Rete algorithm originally developed in the domain of production rule systems for pattern matching in a GT engine. The presented solution supported a simple core graph pattern language.…”

Section: Query Compilation In Graph Transformation Systemsmentioning

confidence: 99%

Model-Driven Engineering of an OpenCypher Engine: Using Graph Queries to Compile Graph Queries

Marton

Szárnyas

Búr

2017

SDL 2017: Model-Driven Engineering for Future Internet

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Abstract. Graph database systems are increasingly adapted for storing and processing heterogeneous network-like datasets. Many challenging applications with near real-time requirements-such as financial fraud detection, on-the-fly model validation and root cause analysis-can be formalised as graph problems and tackled with graph databases efficiently. However, as no standard graph query language has yet emerged, users are subjected to the possibility of vendor lock-in. The openCypher group aims to define an open specification for a declarative graph query language. However, creating an openCypher-compatible query engine requires significant research and engineering efforts. Meanwhile, model-driven language workbenches support the creation of domainspecific languages by providing high-level tools to create parsers, editors and compilers. In this paper, we present an approach to build a compiler and optimizer for openCypher using model-driven technologies, which allows developers to define declarative optimization rules.

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An efficient implementation of graph grammars based on the RETE matching algorithm

Cited by 25 publications

References 9 publications

On the Operationalization of Graph Queries with Generalized Discrimination Networks

On the Operationalization of Graph Queries with Generalized Discrimination Networks

Evaluation of Optimization Strategies for Incremental Graph Queries

Model-Driven Engineering of an OpenCypher Engine: Using Graph Queries to Compile Graph Queries

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