Google Places Enricher: A tool that Makes It Easy to Get and Enrich Google Places API Data
Resumo
The growing number of available APIs means that more developers and interested users need to learn to use unfamiliar interfaces, requiring a learning curve that can compromise productivity. Thus, it becomes important to find ways to facilitate their usability. This work presents a tool that facilitates using the Google Places API, simplifying multiple API calls to cover a region of interest. In addition, the proposed tool also provides features for the enrichment of these data, extending the PoI data from that region with categories from other sources. It is hoped that developers and users without much computer knowledge can benefit from Google Places Enricher, helping to ease the development of new sophisticated urban applications and services.
Palavras-chave:
API, google places, data enrichment, semantic matching
Referências
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Daqing Hou and Lin Li. 2011. Obstacles in using frameworks and APIs: An exploratory study of programmers’ newsgroup discussions. In 2011 IEEE 19th International Conference on Program Comprehension. IEEE, 91–100.
Amy J Ko, Brad A Myers, and Htet Htet Aung. 2004. Six learning barriers in end-user programming systems. In 2004 IEEE Symposium on Visual Languages-Human Centric Computing. IEEE, 199–206.
Géraud Le Falher, Aristides Gionis, and Michael Mathioudakis. 2015. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In Ninth International AAAI Conference on Web and Social Media.
Pablo Martí, Leticia Serrano-Estrada, Almudena Nolasco-Cirugeda, and Jesús López Baeza. 2021. Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods. Journal of Urban Technology (2021), 1–22.
Samuel G McLellan, Alvin W Roesler, Joseph T Tempest, and Clay I Spinuzzi. 1998. Building more usable APIs. IEEE software 15, 3 (1998), 78–86.
Maria N. Pavlova and Asen Alexandrov. 2018. GLOBDEF: a framework for dynamic pipelines of semantic data enrichment tools. In Research Conference on Metadata and Semantics Research. Springer, 159–168.
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/1908.10084
Martin P Robillard. 2009. What makes APIs hard to learn? Answers from developers. IEEE software 26, 6 (2009), 27–34.
Martin P Robillard and Robert DeLine. 2011. A field study of API learning obstacles. Empirical Software Engineering 16, 6 (2011), 703–732.
Mehmet H. Satman and Mustafa Altunbey. 2014. Selecting Location of Retail Stores Using Artificial Neural Networks and Google Places API. International journal of Statistics and Probability 3, 1 (2014), 67.
Rijurekha Sen and Daniele Quercia. 2018. World wide spatial capital. PloS one 13, 2 (2018), e0190346.
Gerhard Fischer. 1987. Cognitive view of reuse and redesign. IEEE Software 4, 4 (1987), 60.
Google 2021. Detalhes de cobertura da Plataforma Google Maps. Google.
Google 2022. Nearby Search. Google.
Yoan Gutiérrez, Sonia Vázquez, and Andrés Montoyo. 2016. A semantic framework for textual data enrichment. Expert Systems with Applications 57 (2016), 248–269.
Daqing Hou and Lin Li. 2011. Obstacles in using frameworks and APIs: An exploratory study of programmers’ newsgroup discussions. In 2011 IEEE 19th International Conference on Program Comprehension. IEEE, 91–100.
Amy J Ko, Brad A Myers, and Htet Htet Aung. 2004. Six learning barriers in end-user programming systems. In 2004 IEEE Symposium on Visual Languages-Human Centric Computing. IEEE, 199–206.
Géraud Le Falher, Aristides Gionis, and Michael Mathioudakis. 2015. Where is the Soho of Rome? Measures and algorithms for finding similar neighborhoods in cities. In Ninth International AAAI Conference on Web and Social Media.
Pablo Martí, Leticia Serrano-Estrada, Almudena Nolasco-Cirugeda, and Jesús López Baeza. 2021. Revisiting the Spatial Definition of Neighborhood Boundaries: Functional Clusters versus Administrative Neighborhoods. Journal of Urban Technology (2021), 1–22.
Samuel G McLellan, Alvin W Roesler, Joseph T Tempest, and Clay I Spinuzzi. 1998. Building more usable APIs. IEEE software 15, 3 (1998), 78–86.
Maria N. Pavlova and Asen Alexandrov. 2018. GLOBDEF: a framework for dynamic pipelines of semantic data enrichment tools. In Research Conference on Metadata and Semantics Research. Springer, 159–168.
Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics. https://arxiv.org/abs/1908.10084
Martin P Robillard. 2009. What makes APIs hard to learn? Answers from developers. IEEE software 26, 6 (2009), 27–34.
Martin P Robillard and Robert DeLine. 2011. A field study of API learning obstacles. Empirical Software Engineering 16, 6 (2011), 703–732.
Mehmet H. Satman and Mustafa Altunbey. 2014. Selecting Location of Retail Stores Using Artificial Neural Networks and Google Places API. International journal of Statistics and Probability 3, 1 (2014), 67.
Rijurekha Sen and Daniele Quercia. 2018. World wide spatial capital. PloS one 13, 2 (2018), e0190346.
Publicado
07/11/2022
Como Citar
GUBERT, Fernanda Regina; SILVA, Thiago H..
Google Places Enricher: A tool that Makes It Easy to Get and Enrich Google Places API Data. In: WORKSHOP DE FERRAMENTAS E APLICAÇÕES - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 28. , 2022, Curitiba.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 91-94.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2022.227245.
