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Documents authored by dos Santos, Vinicius F.

Found 2 Possible Name Variants:

Santos, André Fernandes dos

Document
DOI: 10.4230/OASIcs.SLATE.2021.2
Derzis: A Path Aware Linked Data Crawler

Authors: André Fernandes dos Santos and José Paulo Leal

Published in: OASIcs, Volume 94, 10th Symposium on Languages, Applications and Technologies (SLATE 2021)

Abstract
Consuming Semantic Web data presents several challenges, from the number of datasets it is composed of, to the (very) large size of some of those datasets and the uncertain availability of querying endpoints. According to its core principles, accessing linked data can be done simply by dereferencing the IRIs of RDF resources. This is a light alternative both for clients and servers when compared to dataset dumps or SPARQL endpoints. The linked data interface does not support complex querying, but using it recursively may suffice to gather information about RDF resources, or to extract the relevant sub-graph which can then be processed and queried using other methods. We present Derzis, an open source semantic web crawler capable of traversing the linked data cloud starting from a set of seed resources. Derzis maintains information about the paths followed while crawling, which allows to define property path-based restrictions to the crawling frontier.
Cite as

André Fernandes dos Santos and José Paulo Leal. Derzis: A Path Aware Linked Data Crawler. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 2:1-2:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{santos_et_al:OASIcs.SLATE.2021.2,
  author =	{Santos, Andr\'{e} Fernandes dos and Leal, Jos\'{e} Paulo},
  title =	{{Derzis: A Path Aware Linked Data Crawler}},
  booktitle =	{10th Symposium on Languages, Applications and Technologies (SLATE 2021)},
  pages =	{2:1--2:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-202-0},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{94},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Portela, Filipe and Pereira, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2021.2},
  URN =		{urn:nbn:de:0030-drops-144198},
  doi =		{10.4230/OASIcs.SLATE.2021.2},
  annote =	{Keywords: Semantic web, linked open data, RDF, crawler}
}

Santos, Filipa Alves dos

Document
DOI: 10.4230/OASIcs.SLATE.2021.6
DataGen: JSON/XML Dataset Generator

Authors: Filipa Alves dos Santos, Hugo André Coelho Cardoso, João da Cunha e Costa, Válter Ferreira Picas Carvalho, and José Carlos Ramalho

Published in: OASIcs, Volume 94, 10th Symposium on Languages, Applications and Technologies (SLATE 2021)

Abstract
In this document we describe the steps towards DataGen implementation. DataGen is a versatile and powerful tool that allows for quick prototyping and testing of software applications, since currently too few solutions offer both the complexity and scalability necessary to generate adequate datasets in order to feed a data API or a more complex APP enabling those applications testing with appropriate data volume and data complexity. DataGen core is a Domain Specific Language (DSL) that was created to specify datasets. This language suffered several updates: repeating fields (with no limit), fuzzy fields (statistically generated), lists, highorder functions over lists, custom made transformation functions. The final result is a complex algebra that allows the generation of very complex datasets coping with very complex requirements. Throughout the paper we will give several examples of the possibilities. After generating a dataset DataGen gives the user the possibility to generate a RESTFull data API with that dataset, creating a running prototype. This solution has already been used in real life cases, described with more detail throughout the paper, in which it was able to create the intended datasets successfully. These allowed the application’s performance to be tested and for the right adjustments to be made. The tool is currently being deployed for general use.
Cite as

Filipa Alves dos Santos, Hugo André Coelho Cardoso, João da Cunha e Costa, Válter Ferreira Picas Carvalho, and José Carlos Ramalho. DataGen: JSON/XML Dataset Generator. In 10th Symposium on Languages, Applications and Technologies (SLATE 2021). Open Access Series in Informatics (OASIcs), Volume 94, pp. 6:1-6:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{santos_et_al:OASIcs.SLATE.2021.6,
  author =	{Santos, Filipa Alves dos and Cardoso, Hugo Andr\'{e} Coelho and da Cunha e Costa, Jo\~{a}o and Carvalho, V\'{a}lter Ferreira Picas and Ramalho, Jos\'{e} Carlos},
  title =	{{DataGen: JSON/XML Dataset Generator}},
  booktitle =	{10th Symposium on Languages, Applications and Technologies (SLATE 2021)},
  pages =	{6:1--6:14},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-202-0},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{94},
  editor =	{Queir\'{o}s, Ricardo and Pinto, M\'{a}rio and Sim\~{o}es, Alberto and Portela, Filipe and Pereira, Maria Jo\~{a}o},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2021.6},
  URN =		{urn:nbn:de:0030-drops-144239},
  doi =		{10.4230/OASIcs.SLATE.2021.6},
  annote =	{Keywords: JSON, XML, Data Generation, Open Source, REST API, Strapi, JavaScript, Node.js, Vue.js, Scalability, Fault Tolerance, Dataset, DSL, PEG.js, MongoDB}
}
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