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Cited by 2Graph Theory and AlgorithmsAdvanced Database Systems and QueriesSemantic Web and Ontologies

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Analytical Capabilities of Graphs in Oracle Multimodel Database

Șimonca, Iuliana, Corbea, Alexandra, Belciu, Anda·2022·Smart Innovation, Systems and Technologies
DOI 10.1007/978-981-16-8866-9_9
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Overview

Integrity signals

Registry

Crossref · 10.1007/978-981-16-8866-9_9

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 8 references

Research works printed but not linked: 4

  1. Robinson I, Webber J, Eifrem E (2015) Graph databases: new opportunities for connected data. O'Reilly Media, Inc.
  2. Rishi P, Vandana V (2014) Application of graph theory in computer science and engineering. Int J Comput Appl 104:10–13
  3. Soumya G, Sudheep E, Santhanakrishnan T (2015) A study on applicability of graph databases in big data analysis. Int J Adv Res Trends Eng Technol II:1288–1294
  4. Aven P, Burley D (2017) Building on multi-model databases. O’Reilly Media, USA

Cited by

Metadata

Authors
Șimonca, I., Corbea, A., & Belciu, A.
Affiliations
Bucharest University of Economic Studies (RO)
Corresponding author
Șimonca, Iuliana
ORCID
Venue
Smart Innovation, Systems and Technologies
Publisher
Springer Singapore
Published
2022
Pages
97-109
ISSN
  • 2190-3018 (ISSN-L)
  • 2190-3026
DOI
10.1007/978-981-16-8866-9_9
OpenAlex
W4226432856
Cited by
2
References
8
Citations by year
2024: 2
FWCI
0.94
Type
book chapter
Language
en
Open access
closed
Licence
Locations
Smart innovation, systems and technologies (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Graph Theory and Algorithms
  • Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
graph databases, graph theory, property graphs, RDF graphs, SPARQL, graph analysis, cluster detection, semi-structured data

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.