MillenniumDB: A Persistent, Open-Source, Graph Database

Vrgoč, Domagoj; Rojas, Carlos; Angles, Renzo; Arenas, Marcelo; Arroyuelo, Diego; Aranda, Carlos Buil; Hogan, Aidan; Navarro, Gonzalo; Riveros, Cristian; Romero, Juan

doi:10.48550/arxiv.2111.01540

Cited by 4 publications

(6 citation statements)

References 27 publications

(62 reference statements)

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“…We selected a subset of the Wikidata Truthy from 2021-06-23, which has all but direct properties (i.e. http://www.wikidata.org/prop/direct/P*) removed [18,35]. The data is available to download from Google Drive.…”

Section: Discussionmentioning

confidence: 99%

Differential privacy and SPARQL

Buil-Aranda,

Lobo,

Olmedo

2024

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Differential privacy is a framework that provides formal tools to develop algorithms to access databases and answer statistical queries with quantifiable accuracy and privacy guarantees. The notions of differential privacy are defined independently of the data model and the query language at steak. Most differential privacy results have been obtained on aggregation queries such as counting or finding maximum or average values, and on grouping queries over aggregations such as the creation of histograms. So far, the data model used by the framework research has typically been the relational model and the query language SQL. However, effective realizations of differential privacy for SQL queries that required joins had been limited. This has imposed severe restrictions on applying differential privacy in RDF knowledge graphs and SPARQL queries. By the simple nature of RDF data, most useful queries accessing RDF graphs will require intensive use of joins. Recently, new differential privacy techniques have been developed that can be applied to many types of joins in SQL with reasonable results. This opened the question of whether these new results carry over to RDF and SPARQL. In this paper we provide a positive answer to this question by presenting an algorithm that can answer counting queries over a large class of SPARQL queries that guarantees differential privacy, if the RDF graph is accompanied with semantic information about its structure. We have implemented our algorithm and conducted several experiments, showing the feasibility of our approach for large graph databases. Our aim has been to present an approach that can be used as a stepping stone towards extensions and other realizations of differential privacy for SPARQL and RDF.

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Section: Discussionmentioning

confidence: 99%

Differential privacy and SPARQL

Buil-Aranda,

Lobo,

Olmedo

2024

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“…In addition, Jena Fuseki uses the common query language SPARQL while Neo4j or JanusGraph use the less common languages Cypher and Gremlin. Based on these mentioned arguments Jena Fuseki is implemented within the infrastructure, however, a more extensive analysis of the suitability of Apache Jena Fuseki will be conducted in the future [ 48 ]. Apache Jena Fuseki is implemented on a dedicated server in a docker container and offers the ability to receive and answer SPARQL queries.…”

Section: Methodsmentioning

confidence: 99%

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

Lehmann

Schorz

Rache

et al. 2023

Sensors

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One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

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“…The GraphDB execution speed is slightly slower, at up to 1,000,000 triples compared to Virutoso, but it exceeds this after that [80]. Furthermore, GraphDB uses SPARQL, a common query language, while other alternatives such as Neo4j or JanusGraph use Cypher or Gremlin, which are less common [81]. Based on the arguments outlined above, GraphDB was implemented as a marketplace knowledge graph due to its better performance and common query language.…”

Section: Implementation and Proof Of Conceptmentioning

confidence: 99%

The Anatomy of the Internet of Digital Twins: A Symbiosis of Agent and Digital Twin Paradigms Enhancing Resilience (Not Only) in Manufacturing Environments

et al. 2023

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Due to the growing environmental and geopolitical challenges nowadays, which are causing supply chain complications, industry and society are facing significant new objections. As a complement and extension to the technology-driven premises of Industry 4.0, the value-driven Industry 5.0 focuses on society and the environment. Human centricity, sustainability, and resilience should become a more integral part of both industrial and societal revolutions. One of the enabler technologies for both is the Digital Twin (DT). In order to make DTs intelligent, they must become active, online, goal-seeking, and anticipatory. To meet these requirements, the characteristics of Multi-Agent Systems (MASs) can be employed. This paper contributes to the bilateral emergence of the two industrial paradigms and establishes an approach for the provision of Intelligent Digital Twins (IDTs) within the Internet of Digital Twins (IoDT). Initially, a DT reference model aligned with already established Industry 4.0 reference models enriched with the goals of Industry 5.0 is developed, followed by an outline of how IDTs can be realized with the characteristics of MAS. The work is substantiated by an architectural design for IDTs choreographing marketplace-oriented production processes with a subsequent prototypical implementation, followed by a proof of concept.

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MillenniumDB: A Persistent, Open-Source, Graph Database

Cited by 4 publications

References 27 publications

Differential privacy and SPARQL

Differential privacy and SPARQL

Establishing Reliable Research Data Management by Integrating Measurement Devices Utilizing Intelligent Digital Twins

The Anatomy of the Internet of Digital Twins: A Symbiosis of Agent and Digital Twin Paradigms Enhancing Resilience (Not Only) in Manufacturing Environments

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