Functional querying in graph databases

Pokorný, Jaroslav

doi:10.1007/s40595-017-0104-6

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…Graph databases are focused on efficient storing and querying highly connected data (Pokornỳ, 2018). For relationship-centric data definition, query, and manipulation, graph database provides pattern matching query language that focuses on the relationships between entities.…”

Section: Graph Database: Neo4jmentioning

confidence: 99%

Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Hong

Chen

2021

International Journal of Information Systems and Supply Chain Management

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Supply chain network in the automotive industry has complex, interconnected, multiple-depth relationships. Recently, the volume of supply chain data increases significantly with Industry 4.0. The complex relationships and massive volume of supply chain data can cause visibility and scalability issues in big data analysis and result in less responsive and fragile inventory management. The authors develop a graph data modeling framework to address the computational problem of big supply chain data analysis. In addition, this paper introduces Time-to-Stockout analysis for supply chain resilience and shows how to compute it through a labeled property graph model. The computational result shows that the proposed graph data model is efficient for recursive and variable-length data in supply chain, and relationship-centric graph query language has capable of handling a wide range of business questions with impressive query time.

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Section: Graph Database: Neo4jmentioning

confidence: 99%

Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Hong

Chen

2021

International Journal of Information Systems and Supply Chain Management

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“…(4) Both [25] and [26] consider queries with property graphs and name among others Cypher and Gremlin as important graph query languages. These sources name these categories of graph queries:…”

Section: • Aggregationmentioning

confidence: 99%

Optimization of Retrieval Algorithms on Large Scale Knowledge Graphs

Dörpinghaus¹,

Stefan²

2020

Preprint

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Knowledge graphs have been shown to play an important role in recent knowledge mining and discovery, for example in the field of life sciences or bioinformatics. Although a lot of research has been done on the field of query optimization, query transformation and of course in storing and retrieving large scale knowledge graphs the field of algorithmic optimization is still a major challenge and a vital factor in using graph databases. Few researchers have addressed the problem of optimizing algorithms on large scale labeled property graphs. Here, we present two optimization approaches and compare them with a naive approach of directly querying the graph database. The aim of our work is to determine limiting factors of graph databases like Neo4j and we describe a novel solution to tackle these challenges. For this, we suggest a classification schema to differ between the complexity of a problem on a graph database. We evaluate our optimization approaches on a test system containing a knowledge graph derived biomedical publication data enriched with text mining data. This dense graph has more than 71M nodes and 850M relationships. The results are very encouraging and -depending on the problem -we were able to show a speedup of a factor between 44 and 3839.

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“…If M/(R1,…,Rn), then components M [1],…,M[n] are also terms of respective types R1,…, Rn. The language LT provides a powerful tool for querying graph data conceived as functions [5]. λ-abstractions are important here.…”

Section: Functional Approach To Data Modellingmentioning

confidence: 99%

Integration of Relational and Graph Databases Functionally

Pokorný

2019

Foundations of Computing and Decision Sciences

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A significant category of NoSQL approaches is known as graph databases. They are usually represented by one property graph. We introduce a functional approach to modelling relations and property graphs. Single-valued and multivalued functions will be sufficient in this case. Then, a typed λcalculus, i.e., the language of λ-terms, will be used as a data manipulation language. Some integration options at the query language level are discussed.

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Functional querying in graph databases

Cited by 8 publications

References 14 publications

Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Optimization of Retrieval Algorithms on Large Scale Knowledge Graphs

Integration of Relational and Graph Databases Functionally

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