Visualização de dados para extração de conhecimento: um estudo de caso

Daniel Sadao Matsuba; Adriana Prest Mattedi

doi:10.5380/atoz.v10i2.79184

Authors

Daniel Sadao Matsuba https://orcid.org/0000-0001-8954-2436
Adriana Prest Mattedi Universidade Federal de Itajubá http://orcid.org/0000-0002-4605-9134

DOI:

https://doi.org/10.5380/atoz.v10i2.79184

Keywords:

Data Visualization, Dashboard, Visual Data Mining.

Abstract

Introduction: The fast growth in the volume of data collected over recent years has been making the process of knowledge extraction and analysis increasingly complex. Organizations are facing hardship in combining this large volume of data in useful analysis to support their decision-making. Goal: Evaluate the implementation of visual data mining tools in an insurance startup (insurtech) for smartphones. Method: Dynamic dashboards were developed using Tableau software. It divides the system's feed data into Measurements and Dimensions categories for each chosen analysis theme. It carries out a usability survey with users of the dashboards. Results: The aggregation of several sub-screens and information in the same dashboard was important for users to see new patterns. Conclusions: The introduction of new knowledge discovery tools has enabled users to come to a deeper understanding of the topic and to make a better analysis of patterns.

Author Biographies

Daniel Sadao Matsuba

Bacharel em Sistemas de Informação pela Universidade Federal de Itajubá.

Adriana Prest Mattedi, Universidade Federal de Itajubá

Formada em Ciências Econômicas, Mestrado em análise de sistemas e Doutorado em Computação Aplicada. Atualmente, é professor associado no Instituto de Matemática e Computação da Universidade Federal de Itajubá.

References

Alves, M. C. (2015). Visualização de informação para simplificar o entendimento de indicadores sobre avaliação da ciência e tecnologia (Dissertação de Mestrado). Universidade Federal de São Carlos, São Carlos, SP, Brasil. Recuperado de https://repositorio.ufscar.br/bitstream/handle/ufscar/1151/6802.pdf?sequence=1&isAllowed=y.

Andreyeva, T., Long, M. W., & Brownell, K. D. (2010). The impact of food prices on consumption: a systematic review of research on the price elasticity of demand for food. American journal of public health, 100(2), 216-222. doi: https://doi.org/10.2105/AJPH.2008.151415.

Ankerst, M., Ester, M., & Kriegel, H. P. (2000). Towards an effective cooperation of the user and the computer for classification. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, (pp. 179-188). doi: https://doi.org/10.1145/347090.347124.

Ankerst, M., Keim, D. A., & Kriegel, H. P. (1996). Circle segments: A technique for visually exploring large multidimensional data sets. In Proc. Visualization'96, Hot Topic Session, San Francisco, CA.

Badjio, F. E., & Poulet, F. (2005). Dimension reduction for visual data mining. In Intl. symp. on applied stochastic models and data analysis (ASMDA-2005 Proceedings).

Bramer, M. (2007). Principles of data mining (Vol. 180). London: Springer.

Braun, A., & Schreiber, F. (2017). The current InsurTech landscape: business models and disruptive potential (Vol. 62). Institute of Insurance Economics I. VW-HSG, University of St. Gallen.

Caetano, B. P.; Ribeiro, F. C.; Paula, M. M. V. de & A. Mattedi, A. (2016). A proposal for visualization techniques recommendation to represent survey data. In 11th Iberian Conference on Information Systems and Technologies (CISTI), (pp. 1-6). doi: https://doi.org/10.1109/CISTI.2016.7521633.

Carvalho, D. R., & Dallagassa, M. R. (2014). Mineração de dados: aplicações, ferramentas, tipos de aprendizado e outros subtemas. AtoZ: novas práticas em informação e conhecimento, 3(2), 82-86. doi: https://doi.org/10.5380/atoz.v3i2.41340.

.

Coutinho, G. L. (2014). A Era dos Smartphones: Um estudo exploratório sobre o uso dos Smartphones no Brasil (Monografia de Graduação). Faculdade de Comunicação Social, Universidade de Brasília, Brasília, DF, Brasil. Recuperado de https://bdm.unb.br/bitstream/10483/9405/1/2014_GustavoLeuzingerCoutinho.pdf.

DeSanctis, G., & Jarvenpaa, S. L. (1985, December 16- 18). An Investigation of the" Tables Versus Graphs" Controversy in a Learning Environment. In Proceedings of the 6th International Conference on Information Systems, Indianapolis, (pp. 134-144).

Keim, D., & Ward, M. (2002). Visual data mining techniques. In Hand, D., & Berthold, M. (Eds.). Intelligent Data Analysis, an Introduction (2nd ed.). Springer, Heidelberg.

Marghescu, D., Rajanen, M., & Back, B. (2004). Evaluating the quality of use of visual data-mining tools. In Proc. 11th Europ. Conf. IT Evaluation, (pp. 239-250).

Matsunaga, F. T., Brancher, J. D., & Busto, R. M. (2014). Data mining applications and techniques: A systematic review. Rev. Eletrônica Argentina-Brasil Tecnologias da Informação e da Comunicação, 1(2). doi: https://doi.org/10.5281/zenodo.59454.

Mitchell, O. S., & Utkus, S. P. (Eds.). (2004). Pension design and structure: New lessons from behavioral finance. Oxford University Press.

Muynarsk, R. G., & Miranda, E. de S. (2017). Business Intelligence no agronegócio: um estudo de caso de implementação em uma startup. Revista iPecege, 3(1), 75-84. doi: https://doi.org/0000-0001-8245-1750.

Niggemann, O. (2001). Visual data mining of graph-based data (Dissertation). Department of Mathematics and Computer Science, University of Paderborn, Germany.

O'Halloran, K. L., Tan, S., Pham, D. S., Bateman, J., & Vande Moere, A. (2018). A digital mixed methods research design: Integrating multimodal analysis with data mining and information visualization for big data analytics. Journal of Mixed Methods Research, 12(1), 11-30. doi: https://doi.org/10.1177/1558689816651015.

Rossi, F. (2006). Visual data mining and machine learning. In Proc. 14th European Symposium on Artificial Neural Networks, (pp. 251-264). Bruges, Belgium. Retirado de https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2006-3.pdf.

Salesforce.com, Inc. (2020). Tableau Desktop (2020.3.7). Recuperado de https://www.tableau.com/products/de

Shneiderman, B. (1996, September). The eyes have it: A task by data type taxonomy for information visualizations. In Proc 1996 IEEE symposium on visual languages, (pp. 336-343). doi: https://doi.org/10.1109/VL.1996.545307.

Silva, G.M., Franco, D.J, Dallagranna, G.J., & Cestari, J.M.A.P. (2021). Visualização da informação aplicada em dados aberto nas unidades de saúde municipais de Curitiba-PR: perfil de atendimento de enfermagem. In Coletânea Especial de Engenharia de Produção, (pp. 405-415). Itajubá: Ed. Kreatik.

Vessey, I. (1991). Cognitive fit: A theory based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219-240. doi: https://doi.org/10.1111/j.1540-5915.1991.tb00344.x.

Zuk, T., Schlesier, L., Neumann, P., Hancock, M. S., & Carpendale, S. (2006). Heuristics for information visualization evaluation. In BELIV '06: Proceedings 2006 AVI workshop on BEyond time and errors: novel evaluation methods for information visualization, (pp.1-6). doi: https://doi.org/10.1145/1168149.1168162.