Labeled Property Graphs: SQL or NoSQL?

Anikin, Dmitry; Borisenko, Oleg; Nedumov, Yaroslav

doi:10.1109/ivmem.2019.00007

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Because of their ability to scale horizontally, NoSQL databases cannot guarantee the ACID property. In this regard, the Neo4j graph database [51,52] is SQL (Oracle, MySQL, and SQL-Server) and NoSQL (MongoDB, Neo4j) databases have been the subject of numerous studies comparing their structure, design, and per-formance . These studies used the proposed techniques for situations involving SQL and NoSQL databases and analyzed the outcomes.…”

Section: Empirical Studies Analysismentioning

confidence: 99%

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SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

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The competent software architecture plays a crucial role in the difficult task of big data processing for SQL and NoSQL databases. SQL databases were created to organize data and allow for horizontal expansion. NoSQL databases, on the other hand, support horizontal scalability and can efficiently process large amounts of unstructured data. Organizational needs determine which paradigm is appropriate, yet selecting the best option is not always easy. Differences in database design are what set SQL and NoSQL databases apart. Each NoSQL database type also consistently employs a mixed-model approach. Therefore, it is challenging for cloud users to transfer their data among different cloud storage services (CSPs). There are several different paradigms being monitored by the various cloud platforms (IaaS, PaaS, SaaS, and DBaaS). The purpose of this SLR is to examine the articles that address cloud data portability and interoperability, as well as the software architectures of SQL and NoSQL databases. Numerous studies comparing the capabilities of SQL and NoSQL of databases, particularly Oracle RDBMS and NoSQL Document Database (MongoDB), in terms of scale, performance, availability, consistency, and sharding, were presented as part of the state of the art. Research indicates that NoSQL databases, with their specifically tailored structures, may be the best option for big data analytics, while SQL databases are best suited for online transaction processing (OLTP) purposes.

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Section: Empirical Studies Analysismentioning

confidence: 99%

“…Because of their ability to scale horizontally, NoSQL databases cannot guarantee the ACID property. In this regard, the Neo4j graph database [51,52] is an excellent option. Graph databases [53][54][55] have been shown to effectively organize and store data with complex dependencies.…”

Section: Empirical Studies Analysismentioning

confidence: 99%

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

Khan

Kumar

Cheng

et al. 2023

BDCC

View full text Add to dashboard Cite

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“…Instead, institutions operating with Labeled Property Graphs generate their semantics as there are no ideal ontologies. Each Labeled Property Graph attains its identical querying language [51]. In summary, by assembling properties and labels to the nodes and edges, the Labeled Property Graph gives the ability to access more information about the nodes and edges directly.…”

Section: Labeled Property Graphs (Lpg)mentioning

confidence: 99%

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

Mst. Mim Akter,

Rahoman

2023

Artificial Intelligence Evolution

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A Knowledge Graph is a directive graph where the nodes state the entities and the edges describe the relationships between the entities of data. It is also referred to as a Semantic Network. The massive amount of data generated every day can be transformed into knowledge via knowledge graphs for the effective use of these data. Knowing the classification of Knowledge Graphs is required to adapt to different requirements of Knowledge Graphs. Knowledge Graphs are primarily classified concerning their building techniques and their usages. In building techniques, it is considered how the Knowledge Graph is built. For example, the graph can be constructed as a triplet, quadruplet, etc., or created from structured data, e.g., database, or unstructured data, e.g., text, image, etc. On the other hand, Knowledge Graphs can be used for various purposes. For example, Knowledge Graphs can be used for Information Retrieval, Semantic Query, etc., or different types of data visualization. Nowadays, Knowledge Graph is one of the trending topics in the modern technology-dependent world. However, clear and specific discussions on the classifications of Knowledge Graphs and their various usages are less available. In this paper, we will describe the classification of knowledge graphs and their various usages in detail so that the readers can get a clear concept of this topic.

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“…edges within a graph database is faster (Robinson et al , 2013) than querying with RDBMS because the relationship is stored within the database itself. Some graph database examples are Neo4j (Lal, 2015), OrientDB (Tesoriero, 2013), JanusGraph (Anikin et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

A novel framework for delivering static search capabilities to large textual corpora directly on the Web domain: an implementation for Migne’s Patrologia Graeca

Varthis

Poulos

Giarenis

et al. 2021

IJWIS

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Purpose This study aims to provide a system capable of static searching on a large number of unstructured texts directly on the Web domain while keeping costs to a minimum. The proposed framework is applied to the unstructured texts of Migne’s Patrologia Graeca (PG) collection, setting PG as an implementation example of the method. Design/methodology/approach The unstructured texts of PG have automatically transformed to a read-only not only Structured Query Language (NoSQL) database with a structure identical to that of a representational state transfer access point interface. The transformation makes it possible to execute queries and retrieve ranked results based on a specialized application of the extended Boolean model. Findings Using a specifically built Web-browser-based search tool, the user can quickly locate ranked relevant fragments of texts with the ability to navigate back and forth. The user can search using the initial part of words and by ignoring the diacritics of the Greek language. The performance of the search system is comparatively examined when different versions of hypertext transfer protocol (Http) are used for various network latencies and different modes of network connections. Queries using Http-2 have by far the best performance, compared to any of Http-1.1 modes. Originality/value The system is not limited to the case study of PG and has a generic application in the field of humanities. The expandability of the system in terms of semantic enrichment is feasible by taking into account synonyms and topics if they are available. The system’s main advantage is that it is totally static which implies important features such as simplicity, efficiency, fast response, portability, security and scalability.

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Labeled Property Graphs: SQL or NoSQL?

Cited by 5 publications

References 7 publications

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

SQL and NoSQL Database Software Architecture Performance Analysis and Assessments—A Systematic Literature Review

A Systematic Review on Knowledge Graphs Classiﬁcation and Their Various Usages

A novel framework for delivering static search capabilities to large textual corpora directly on the Web domain: an implementation for Migne’s Patrologia Graeca

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