The use of heterogeneous geospatial big data for improving decision-making

• Flávio E. A. Horita USP / UFABC
• João Porto de Albuquerque USP / University of Warwick

DOI: https://doi.org/10.5753/ctd.2018.3663

Abstract

Emerging data sources have been increasing the amount of available and useful data, which have a potential for revolutionizing entire business processes and decisions in several scenarios, e.g., smart cities. However, at the same time that these “big data” open further opportunities, the heterogeneity of their features often hampers the integration and visualization of data. Therefore, this work presents an approach to handle heterogeneous geospatial big data for supporting a more informative decision-making. Study results advanced the state-of-the-art by understanding decision-makers' requirements and developing innovative decision support system. These indeed provided valuable contributions to practice and research.

References

Assis, L. F. F. G., Behnck, L. P., Doering, D., Freitas, E. P., Pereira, C. E., Horita, F. E. A., Ueyama, J., and Albuquerque, J. P. (2016). Dynamic sensor management: Extending sensor web for near real-time mobile sensor integration in dynamic scenarios. In Proceedings of the 30th International Conference on Advanced Information Networking and Applications (AINA), pages 303–310.

Gregor, S. and Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2):337–356.

Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., and Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47:98–115.

Horita, F. E., Albuquerque, J. P., Marchezini, V., and Mendiondo, E. M. (2017). Bridging the gap between decision-making and emerging big data sources: An application of a model-based framework to disaster management in brazil. Decision Support Systems, 97:12–22.

Horita, F. E., de Albuquerque, J. P., and Marchezini, V. (2018). Understanding the decision-making process in disaster risk monitoring and early-warning: A case study within a control room in brazil. International Journal of Disaster Risk Reduction, 28:22–31.

Horita, F. E. A., Albuquerque, J. P., Degrossi, L. C., Mendiondo, E. M., and Ueyama, J. (2015). Development of a spatial decision support system for flood risk management in brazil that combines volunteered geographic information with wireless sensor networks. Computers & Geosciences, 80:84–94.

Horita, F. E. A., Degrossi, L. C., Assis, L. F. G., Zipf, A., and Albuquerque, J. P. (2013). The use of volunteered geographic information (VGI) and crowdsourcing in disaster management: a systematic literature review. In Proceedings of the 19th Americas Conference on Information Systems (AMCIS), pages 1–10.

Kleindienst, D., Pfleger, R., and Schoch, M. (2015). The business alignment of social media analytics. In Proceedings of the 23rd European Conference on Information Systems (ECIS), pages 1–14.

Santoro, F. M., Borges, M. R. S., and Pino, J. A. (2010). Acquiring knowledge on business processes from stakeholders’ stories. Advanced Engineering Informatics, 24:138–148.

Schnebele, E., Cervone, G., and Waters, N. (2014). Road assessment after flood events using non-authoritative data. Natural Hazards and Earth System Sciences, 14(4):1007–1015.

UN (2015). Sendai Framework for Disaster Risk Reduction 2015-2030. Technical report, United Nations. Technical Report.

Vieweg, S., Castillo, C., and Imran, M. (2014). Integrating social media communications into the rapid assessment of sudden onset disasters. In Proceedings of the 2014 International Conference on Social Informatics (SocInfo), pages 444–461.

Wamba, S. F., Akter, S., Edwards, A., Chopin, G., and Gnanzou, D. (2015). How “big data” can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165:234–246.