Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives
Abstract
In the past few years, improvements in the data acquisition technology have decreased the time interval of data gathering. Consequently, institutions have stored huge amounts of data such as climate time series and remote sensing images. Computational models to filter, transform, merge and analyze data from many different areas are complex and challenging. The complexity increases even more when combining several knowledge domains. Examples are research in climatic changes, biofuel production and environmental problems. A possible solution to the problem is the association of several computational techniques. Accordingly, this paper presents a framework to analyze, monitor and visualize climate and remote sensing data by employing methods based on fractal theory, data mining and visualization techniques. Initial experiments showed that the information and knowledge discovered from this framework can be employed to monitor sugar cane crops, helping agricultural entrepreneurs to make decisions in order to become more productive. Sugar cane is the main source to ethanol production in Brazil, and has a strategic importance for the country economy and to guarantee the Brazilian self-sufficiency in this important, renewable source of energy.References
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Traina Jr., C., Sousa, E. P. M. d., and Traina, A. J. M. (2005). Using fractals in data mining. In Kantardzic, M. M. and Zurada, J., editors, New Generation of Data Mining Applications, volume 1, pages 599–630 (Chapter 24). Wiley/IEEE Press.
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Zaki, M. J. (2001). Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1-2):31–60.
Baioco, G. B., Traina, A. J. M., and Traina, Caetano, J. (2007). Mamcost: Global and local estimates leading to robust cost estimation of similarity queries. In SSDBM 2007, pages 6–16, Banff, Canada. ACM Press.
Barbará, D. and Chen, P. (2000). Using the fractal dimension to cluster datasets. In ACM SIGKDD, pages 260–264, Boston, MA.
Chakrabarti, D. and Faloutsos, C. (2002). F4: large-scale automated forecasting using fractals. In CIKM, volume 1, pages 2–9, McLean, VA - EUA. ACM Press.
Chiu, B., Keogh, E., and Lonardi, S. (2003). Probabilistic discovery of time series motifs. In SIGKDD, pages 493–498.
Emery, W. J., Brown, J., and Novak, Z. P. (1989). Avhrr image navigation: summary and review. Photogrammetric Engineering and Remote Sensing., 55(8):1175–1183.
Esquerdo, J. C. D. M., Antunes, J. F. G., Baldwin, D. G., Emery, W. J., and Zullo Jr, J. (2006). An automatic system for avhrr land surface product generation. IJRS, 27(18):3925–3942.
Faloutsos, C. and Kamel, I. (1994). Beyond uniformity and independence: Analysis of r-trees using the concept of fractal dimension. In ACM PODS, pages 4–13, Minneapolis, MN.
Harms, S. K. and Deogun, J. S. (2004). Sequential association rule mining with time lags. JIIS, 22(1):7–22.
Holben, B. N. (1986). Characteristics of maximum value composite images from temporal avhrr data. IJRS, 7:1417–1435.
Holben, B. N., Tucker, C. J., and Cheng-Jeng, F. (1980). Spectral assessment of soyabean leaf area and leaf biomass. Photogrammetric Engineering and Remote Sensing, 46(5):651–656.
Honda, R. and Konishi, O. (2001). Temporal rule discovery for time-series satellite images and integration with rdb. In PKDD, pages 204–215, Freiburg, Germany. Springer-Verlag.
IPCC (2007). Intergovernmental panel on climate change. [link]. accessed: March, 2009.
Julea, A., Méger, N., and Trouvé, E. (2006). Sequential patterns extraction in multitemporal satellite images. In PKDD, pages 96–99, Berlin, Germany. Springer-Verlag.
Keim, D. A., Mansmann, F., Schneidewind, J., and Ziegler, H. (2006). Challenges in visual data analysis. In IV ’06, pages 9–16.
Keogh, E. and Kasetty, S. (2002). On the need for time series data mining benchmarks: A survey and empirical demonstration. In SIGKDD, pages 102–111.
Pagel, B.-U., Korn, F., and Faloutsos, C. (2000). Deflating the dimensionality curse using multiple fractal dimensions. In ICDE, pages 589–598, San Diego, CA. IEEE Computer Society.
Pearson, K. (1896). Mathematical contributions to the theory of evolution. iii regression, heredity and panmixia. Philos Trans Royal Soc London Ser A, 187:253–318.
Ribeiro, M. X., Traina, A. J. M., and Jr., C. T. (2008). A new algorithm for data discretization and feature selection. In ACM SAC, pages 953–954, Fortaleza, Ceara, Brazil.
Rodrigues Jr., J. F., Traina Jr., C., and Traina, A. J. M. (2008). Metricssplat - the metric space platform. [link]. accessed: March, 2009.
Romani, L. A. S., Sousa, E. P. M., Ribeiro, M. X., Zullo Jr., J., Traina Jr., C., and Traina, A. J. M. (2009a). Employing fractal dimension to analyze climate and remote sensing data streams. In SIAM SDM-MDM, pages 1–12, Sparks Nevada, USA.
Romani, L. A. S., Sousa, E. P. M., Zullo Jr., J., Traina Jr., C., and Traina, A. J. M. (2009b). Aplicação de método baseado em fractais para detecção de correlações entre imagens avhrr-noaa e dados agroclimáticos em regiões produtoras de cana-de-açúcar. In SBSR, Natal, Brasil.
Rosborough, G. W.; Baldwin, D. G. E. W. J. (1994). Precise avhrr image navigation. IEEE Transactions on Geoscience and Remote Sensing, 32(3):644–657.
Rosseti, L. (2001). Zoneamento agrícola em aplicações de crédito e securidade rural no brasil: Aspectos atuariais e de política agrícola. RBAgro, 9(3):386–399.
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W. (1973). Monitoring vegetation systems in the great plains with erts. In Earth Resources TechnologySatellite, volume 1 of NASA SP-351, pages 309–317, Washington, D. C. NASA. Goddart Space Flight Center.
Schroeder, M. (1991). Fractals, Chaos, Power Laws. W. H. Freeman, New York, 6 edition.
Sousa, E. P. M. d., Traina, Caetano, J., Traina, A. J. M., and Faloutsos, C. (2007a). Measuring evolving data streams’ behavior through their intrinsic dimension. New Generation Computing Journal, 25:33–59.
Sousa, E. P. M. d., Traina, Caetano, J., Traina, A. J. M., Wu, L., and Faloutsos, C. (2007b). A fast and effective method to find correlations among attributes in databases. DMKD, 14(3):367 – 407.
Thomas, J. and Cook, K. (2005). Illuminating the path: Research and development agenda for visual analytics. In IEEE-Press.
Thornthwaite, C. W. and Mather, J. R. (1955). The water balance. Climatology, 8(1):104.
Traina Jr., C., Sousa, E. P. M. d., and Traina, A. J. M. (2005). Using fractals in data mining. In Kantardzic, M. M. and Zurada, J., editors, New Generation of Data Mining Applications, volume 1, pages 599–630 (Chapter 24). Wiley/IEEE Press.
Traina Jr., C., Traina, A. J. M., Wu, L., and Faloutsos, C. (2000). Fast feature selection using fractal dimension. In SBBD, pages 158–171, João Pessoa, PB.
Wu, T., Song, G., X., M., X., G., and Jin, X. (2008). Mining geographic episode association patterns of abnormal events in global earth science data. Science in China, 51:155–164.
Zaki, M. J. (2001). Spade: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1-2):31–60.
Published
2009-07-20
How to Cite
ROMANI, Luciana A. S.; TRAINA, Agma J. M.; SOUSA, Elaine P. M. de; ZULLO JR., Jurandir; AVILA, Ana M. H.; RODRIGUES JR., Jose Fernando; TRAINA JR, Caetano.
Computational framework to analyze agrometeorological, climate and remote sensing data: challenges and perspectives. In: INTEGRATED SOFTWARE AND HARDWARE SEMINAR (SEMISH), 36. , 2009, Bento Gonçalves/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2009
.
p. 323-337.
ISSN 2595-6205.
