Uma taxonomia de Visual Analytics em Sistemas de Apoio à Decisão
Resumo
Sistemas de Apoio à Decisão fazem parte do dia a dia de todos os ambientes, atuando no suporte ao processo de tomada de decisão, auxilia na utilização de dados como embasamento, possibilitando decisões mais eficientes e vantajosas. O aumento do volume de dados produzidos digitalmente traz inúmeras oportunidades e as organizações tem aperfeiçoado seus SAD como meio de descobrir novas informações ocultas nestes dados. Como meio de resolver o problema de sobrecarga de dados com uma das dificuldades, o Visual Analytics (VA) é aplicado para um melhor uso e exploração desses dados. No entanto, a implementação de VA em um SAD não é uma tarefa trivial, exigindo atualmente técnicas de desenvolvimento e avaliação. O presente trabalho tem como objetivo apresentar uma taxonomia de VA em SAD. Para tanto, o projeto adotou uma metodologia sistemática para o estabelecimento da taxonomia. A principal contribuição deste projeto é apresentar uma taxonomia que possa classificar e descrever arquiteturas dos SAD que implementam VA.
Palavras-chave:
Taxonomia, Sistema de Apoio à Decisão, Visual Analytics, Engenharia de Software
Referências
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Al-Serafi, A. and Elragal, A. (2014). Visual trajectory pattern mining: An exploratory study in baggage handling systems. In Advances in Data Mining. Applications and Theoretical Aspects, pages 159–173, St. Petersburg. Springer International Publishing.
Basili, V. (1994). GQM approach has evolved to include models. IEEE Software, 11(1).
Cook, K. A., Scholtz, J., and Whiting, M. A. (2015). A software developer’s guide to informal evaluation of visual analytics environments using vast challenge information. In 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 193–194, Chicago, USA. IEEE, IEEE.
Daradkeh, M. K. (2019). Determinants of visual analytics adoption in organizations. Information Technology & People.
Eaglin, T., Wang, X., Ribarsky, W., and Tolone, W. (2015). Ensemble visual analysis architecture with high mobility for large-scale critical infrastructure simulations. In Visualization and Data Analysis 2015, volume 9397, page 939706, San Francisco-CA, USA. International Society for Optics and Photonics, SPIE.
Gonzales, G. R. and Horita, F. E. A. (2020). Visual analytics in decision support system architecture: A systematic mapping study. In Proceedings of the XIX Brazilian Symposium on Human Factors in Computational Systems, pages 140–149, New York, NY, USA. ACM.
González-Torres, A., Navas-Sú, J., Hernández-Vásquez, M., Hernández-Castro, F., and Solano-Cordero, J. (2019). A visual analytics architecture for the analysis and understanding of software systems. Enfoque UTE, 10(1):218–233.
Greitzer, F. L., Noonan, C. F., and Franklin, L. (2011). Cognitive foundations for visual analytics. Technical report, Pacific Northwest National Lab.(PNNL), Richland, WA(United States).
Horita, F. E. and de Albuquerque, J. P. (2013). An approach to support decision-making in disaster management based on volunteer geographic information (vgi) and spatial decision support systems (sdss). In ISCRAM.
Karacapilidis, N. (2006). An overview of future challenges of decision support technologies. Intelligent Decision-making Support Systems, pages 385–399.
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual analytics: Scope and challenges. In Visual data mining, pages 76–90. Springer, Berlin, Germany.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., and Linkman, S.(2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1):7–15.
Mendonça, E. A. (2004). Clinical decision support systems: perspectives in dentistry. Journal of dental education, 68(6):589–597.
Nickerson, R. C., Varshney, U., and Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3):336–359.
Park, H., Bellamy, M. A., and Basole, R. C. (2016). Visual analytics for supply network management: System design and evaluation. Decision Support Systems, 91:89–102.
Pearson, J. M. and Shim, J. (1994). An empirical investigation into decision support systems capabilities: a proposed taxonomy. Information & Management, 27(1):45–57.
Pearson, J. M. and Shim, J. (1995). An empirical investigation into dss structures andenvironments. Decision Support Systems, 13(2):141–158.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Ease, volume 8, pages 68–77, Swindon, United Kingdom.
Pettit, C., Bakelmun, A., Lieske, S. N., Glackin, S., Thomson, G., Shearer, H., Dia, H., Newman, P., et al. (2018). Planning support systems for smart cities. City, culture and society, 12:13–24.
Power, D. J. (2004). Specifying an expanded framework for classifying and describing decision support systems. Communications of the Association for Information Systems,13(1):13.
Thomas, J. (2009). Taxonomy for visual analytics: Seeking feedback. VAC Views, May.
Visual Analytics Community (2010). Visual Analytics Taxonomy Draft Taxonomy for Visual Analytics. http://vacommunity.org/Visual+Analytics+Taxonomy. Online; accessed 5 May 2020.
Wu, Y., Cao, N., Gotz, D., Tan, Y.-P., and Keim, D. A. (2016). A survey on visual analytics of social media data. IEEE Transactions on Multimedia, 18(11):2135–2148.
Al-Serafi, A. and Elragal, A. (2014). Visual trajectory pattern mining: An exploratory study in baggage handling systems. In Advances in Data Mining. Applications and Theoretical Aspects, pages 159–173, St. Petersburg. Springer International Publishing.
Basili, V. (1994). GQM approach has evolved to include models. IEEE Software, 11(1).
Cook, K. A., Scholtz, J., and Whiting, M. A. (2015). A software developer’s guide to informal evaluation of visual analytics environments using vast challenge information. In 2015 IEEE Conference on Visual Analytics Science and Technology (VAST), pages 193–194, Chicago, USA. IEEE, IEEE.
Daradkeh, M. K. (2019). Determinants of visual analytics adoption in organizations. Information Technology & People.
Eaglin, T., Wang, X., Ribarsky, W., and Tolone, W. (2015). Ensemble visual analysis architecture with high mobility for large-scale critical infrastructure simulations. In Visualization and Data Analysis 2015, volume 9397, page 939706, San Francisco-CA, USA. International Society for Optics and Photonics, SPIE.
Gonzales, G. R. and Horita, F. E. A. (2020). Visual analytics in decision support system architecture: A systematic mapping study. In Proceedings of the XIX Brazilian Symposium on Human Factors in Computational Systems, pages 140–149, New York, NY, USA. ACM.
González-Torres, A., Navas-Sú, J., Hernández-Vásquez, M., Hernández-Castro, F., and Solano-Cordero, J. (2019). A visual analytics architecture for the analysis and understanding of software systems. Enfoque UTE, 10(1):218–233.
Greitzer, F. L., Noonan, C. F., and Franklin, L. (2011). Cognitive foundations for visual analytics. Technical report, Pacific Northwest National Lab.(PNNL), Richland, WA(United States).
Horita, F. E. and de Albuquerque, J. P. (2013). An approach to support decision-making in disaster management based on volunteer geographic information (vgi) and spatial decision support systems (sdss). In ISCRAM.
Karacapilidis, N. (2006). An overview of future challenges of decision support technologies. Intelligent Decision-making Support Systems, pages 385–399.
Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. (2008). Visual analytics: Scope and challenges. In Visual data mining, pages 76–90. Springer, Berlin, Germany.
Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., and Linkman, S.(2009). Systematic literature reviews in software engineering–a systematic literature review. Information and software technology, 51(1):7–15.
Mendonça, E. A. (2004). Clinical decision support systems: perspectives in dentistry. Journal of dental education, 68(6):589–597.
Nickerson, R. C., Varshney, U., and Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3):336–359.
Park, H., Bellamy, M. A., and Basole, R. C. (2016). Visual analytics for supply network management: System design and evaluation. Decision Support Systems, 91:89–102.
Pearson, J. M. and Shim, J. (1994). An empirical investigation into decision support systems capabilities: a proposed taxonomy. Information & Management, 27(1):45–57.
Pearson, J. M. and Shim, J. (1995). An empirical investigation into dss structures andenvironments. Decision Support Systems, 13(2):141–158.
Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008). Systematic mapping studies in software engineering. In Ease, volume 8, pages 68–77, Swindon, United Kingdom.
Pettit, C., Bakelmun, A., Lieske, S. N., Glackin, S., Thomson, G., Shearer, H., Dia, H., Newman, P., et al. (2018). Planning support systems for smart cities. City, culture and society, 12:13–24.
Power, D. J. (2004). Specifying an expanded framework for classifying and describing decision support systems. Communications of the Association for Information Systems,13(1):13.
Thomas, J. (2009). Taxonomy for visual analytics: Seeking feedback. VAC Views, May.
Visual Analytics Community (2010). Visual Analytics Taxonomy Draft Taxonomy for Visual Analytics. http://vacommunity.org/Visual+Analytics+Taxonomy. Online; accessed 5 May 2020.
Wu, Y., Cao, N., Gotz, D., Tan, Y.-P., and Keim, D. A. (2016). A survey on visual analytics of social media data. IEEE Transactions on Multimedia, 18(11):2135–2148.
Publicado
07/06/2021
Como Citar
GONZALES, Gustavo Romão; DEGROSSI, Lívia Castro; HORITA, Flavio Eduardo Aoki.
Uma taxonomia de Visual Analytics em Sistemas de Apoio à Decisão. In: WORKSHOP DE TESES E DISSERTAÇÕES EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, On-line.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 68-72.
DOI: https://doi.org/10.5753/sbsi.2021.15358.
