@Article{DiPaoloGoFePaViSa:2019:DaMiSp,
               author = "Di Paolo, Italo F. and Gouveia, Nelson de Almeida and Ferreira 
                         Neto, Luiz C. and Paes, Eduardo T. and Vijaykumar, Nandamudi 
                         Lankalapalli and Santana, {\'A}damo L.",
          affiliation = "{Universidade Estadual do Par{\'a} (UEPA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         do Par{\'a} (UFPA)} and {Universidade Federal Rural da 
                         Amaz{\^o}nia (UFRA)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Federal do Par{\'a} (UFPA)}",
                title = "Data mining of spatio-temporal variability of chlorophyll-a 
                         concentrations in a portion of the Western Atlantic with low 
                         performance hardware",
              journal = "Journal of Software Engineering and Applications",
                 year = "2019",
               volume = "12",
               number = "5",
                pages = "149--170",
             keywords = "Data Mining, Clustering, Chlorophyll, Atlantic, Missing Data, 
                         Small Hardware.",
             abstract = "The contemporary scientific literature that deals with the 
                         dynamics of marine chlorophyll-a concentration is already 
                         customarily employing data mining techniques in small geographic 
                         areas or regional samples. However, there is little focus on the 
                         issue of missing data related to chlorophyll-a concentration 
                         estimated by remote sensors. Intending to provide greater scope to 
                         the identification of the spatiotemporal distribution patterns of 
                         marine chlorophyll-a concentrations, and to improve the 
                         reliability of results, this study presents a data mining approach 
                         to cluster similar chlorophyll-a concentration behaviors while 
                         implementing an iterative spatiotemporal interpolation technique 
                         for missing data inference. Although some dynamic behaviors of 
                         said concentrations in specific areas are already known by 
                         specialists, systematic studies in large geographical areas are 
                         still scarce due to the computational complexity involved. For 
                         this reason, this study analyzed 18 years of NASA satellite 
                         observations in one portion of the Western Atlantic Ocean, 
                         totaling more than 60 million records. Additionally, performance 
                         tests were carried out in low-cost computer systems to check the 
                         accessibility of the proposal implemented for use in computational 
                         structures of different sizes. The approach was able to identify 
                         patterns with high spatial resolution, accuracy and reliability, 
                         rendered in low-cost computers even with large volumes of data, 
                         generating new and consistent patterns of spatiotemporal 
                         variability. Thus, it opens up new possibilities for data mining 
                         research on a global scale in this field of application.",
                  doi = "10.4236/jsea.2019.125010",
                  url = "http://dx.doi.org/10.4236/jsea.2019.125010",
                 issn = "1945-3116",
                label = "lattes: 2893215729403643 2 DiPaoloGoNePaViSa:2019:DaMiSp",
             language = "pt",
           targetfile = "paolo_data.pdf",
        urlaccessdate = "20 abr. 2024"
}