Cypher

Francis, Nadime; Green, Alastair; Guagliardo, Paolo; Libkin, Leonid; Lindaaker, Tobias; Marsault, Victor; Plantikow, Stefan; Rydberg, Mats; Selmer, Petra; Taylor, Andrés

doi:10.1145/3183713.3190657

Cited by 305 publications

(80 citation statements)

References 55 publications

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“…Graph databases have become an important solution to consider in the management of large datasets [45]. Using them to manage graph-structured data presents many benefits such as explicit support for modeling graph data, native indexing and storage for fast graph traversal operations, built-in support for graph algorithms (e.g., Page Rank and subgraph matching), and the provision of graph languages, allowing users to express complex pattern-matching operations [46,47]. The main characteristic of a graph database is that the data are conceptually modeled and presented to the user as a graph [48].…”

Section: Storing the Data: Knowledge Graphmentioning

confidence: 99%

“…The labeled property graph [39] Consequently, Neo4j can be used as a high-performance replacement for relational databases, especially when handling highly interconnected data [50]. We use the Cypher query language, which was specific to the Neo4j graph database, but currently, many other different commercial products are also using it [46]. It is a well-established declarative language for querying and updating property graph databases [47].…”

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confidence: 99%

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A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

et al. 2021

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Today, the automotive and transportation sector is undergoing a transformation process to meet the requirements of sustainable and efficient operations. This transformation mainly reveals itself by electric vehicles, hybrid electric vehicles, and electric vehicle sharing. One significant, and the most expensive, component in electric vehicles is the batteries, and the management of batteries is crucial. It is essential to perform constant monitoring of behavior changes for operational purposes and quickly adjust components and operations to these changes. Thus, to address these challenges, we propose a knowledge graph-based data integration framework for simplifying access and analysis of data accumulated through the operations of vehicles and related transportation systems. The proposed framework aims to enable the effortless analysis and navigation of integrated knowledge and the creation of additional data sets from this knowledge to use during the application of data analysis and machine learning. The knowledge graph serves as a significant component to simplify the extraction, enrichment, exploration, and generation of data in this framework. We have developed it according to the human-centered design, and various roles of the data science and machine learning life cycle can use it. Its main objective is to streamline the exploration and interaction with the integrated data to maximize human productivity. Finally, we present a battery use case to show the feasibility and benefits of the proposed framework. The use case illustrates the usage of the framework to extract knowledge from raw data, navigate and enrich it with additional knowledge, and generate data sets.

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Section: Storing the Data: Knowledge Graphmentioning

confidence: 99%

mentioning

confidence: 99%

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

et al. 2021

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“…Therefore, we use our identified languages as seed languages to find related work on graphrelated query languages. Such work (e.g., [4,34]) frequently refers to both Cypher [27,52], the property graph query language initially developed for Neo4j [51], and the Gremlin graph traversal language [25,60]. We do not include languages exclusive to proprietary graph databases, or more recent developments in research, such as G-CORE [3].…”

Section: Graph Query Languagesmentioning

confidence: 99%

“…A long-standing standard is SPARQL [57], a query language for RDF graphs which conceptually matches on the triple structure of RDF. For property graphs, languages like Cypher [27] emerged, featuring specialized constructs to build results by selecting nodes and edges, either of which may potentially hold properties. While the major graph-related query approaches have been compared in terms of various properties of both language [4] and implementation [2,34], we are interested in the practical usage of such technologies.…”

Section: Introductionmentioning

confidence: 99%

Empirical study on the usage of graph query languages in open source Java projects

Seifer

Härtel

Leinberger

et al. 2019

Proceedings of the 12th ACM SIGPLAN International Conference on Software Language Engineering

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Graph data models are interesting in various domains, in part because of the intuitiveness and flexibility they offer compared to relational models. Specialized query languages, such as Cypher for property graphs or SPARQL for RDF, facilitate their use. In this paper, we present an empirical study on the usage of graph-based query languages in opensource Java projects on GitHub. We investigate the usage of SPARQL, Cypher, Gremlin and GraphQL in terms of popularity and their development over time. We select repositories based on dependencies related to these technologies and employ various popularity and source-code based filters and ranking features for a targeted selection of projects. For the concrete languages SPARQL and Cypher, we analyze the activity of repositories over time. For SPARQL, we investigate common application domains, query use and existence of ontological data modeling in applications that query for concrete instance data. Our results show, that the usage of graph query languages in open-source projects increased over the last years, with SPARQL and Cypher being by far the most popular. SPARQL projects are more active in terms of query related artifact changes and unique developers involved, but Cypher is catching up. Relatively few applications use SPARQL to query for concrete instance data: A majority of those applications employ multiple different ontologies,

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“…For this purpose, graph query languages often include constructs such as Regular Path Queries (RPQs) [27], and various extensions such as Conjunctions of them (CR-PQs) and Union of CRPQs (UCRPQs) [19,20,26,50]. For instance, the query language SPARQL 1.1 [43] introduced Property Paths, and language proposals such as OpenCypher [34,55] and G-core [11] also include the possibility of expressing recursive paths. SPARQL's Property Paths revealed crucial for extracting information from RDF data structures such as those found in social networks, life sciences and transportation networks.…”

Section: Introductionmentioning

confidence: 99%

On the Optimization of Recursive Relational Queries: Application to Graph Queries

Jachiet

Genevès

Gesbert

et al. 2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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Graph databases have received a lot of attention as they are particularly useful in many applications such as social networks, life sciences and the semantic web. Various languages have emerged to query graph databases, many of which embed forms of recursion which reveal essential for navigating in graphs. The relational model has benefited from a huge body of research in the last half century and that is why many graph databases rely on techniques of relational query engines. Since its introduction, the relational model has seen various attempts to extend it with recursion and it is now possible to use recursion in several SQL or Datalog based database systems. The optimization of recursive queries remains, however, a challenge. We propose µ-RA, a variation of the Relational Algebra equipped with a fixpoint operator for expressing recursive relational queries. µ-RA can notably express unions of conjunctive regular path queries. Leveraging the fact that this fixpoint operator makes recursive terms more amenable to algebraic transformations, we propose new rewrite rules. These rules makes it possible to generate new query execution plans, that cannot be obtained with previous approaches. We present the syntax and semantics of µ-RA, and the rewriting rules that we specifically devised to tackle the optimization of recursive queries. We report on practical experiments that show that the newly generated plans can provide significant performance improvements for evaluating recursive queries over graphs.

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Cypher

Cited by 305 publications

References 55 publications

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

A Knowledge Graph-Based Data Integration Framework Applied to Battery Data Management

Empirical study on the usage of graph query languages in open source Java projects

On the Optimization of Recursive Relational Queries: Application to Graph Queries

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