DCluster: Geospatial Analytics with PoI Identification
DOI:
https://doi.org/10.5753/jidm.2021.1952Keywords:
Points of Interest, Clustering, Geospatial Data, Data AnalysisAbstract
The generation of geospatial data is an inherent aspect for several applications that aim to track people, automobiles, or other mobile objects. Mining information from this type of data is a crucial factor for the development of Smart Cities. In many cases, it can help improve human mobility and the quality of citizens. In this sense, there is a growing demand for systems capable of extracting information from several data types, including the geospatial one. In this work, we present DCluster, a web system that aims to assist data analysts in exploring and visualizing the main types of data, including the geospatial one. Additionally, DCluster has the capability of discovering points of interest based on data of mobile users and classifying them as Home, Work, and Other locations. Data analysts can take advantage of DCluster to explore their data and extract knowledge from it.
Downloads
References
BigML. Bigml: Machine learning made easy. https://bigml.com/, 2011. Accessed on 11/2/2021.
Capanema, C., Silva, F. A., and Braga, T. M. Identificacão e classificacão de pontos de interesse individuais com base em dados esparsos. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC, pp. 15–28, 2019.
Capanema, C. G. S., Silva, F., and Silva, T. Dcluster: Um sistema para análise exploratória de grandes volumes de dados georreferenciados. In Satellite Events of the 32nd Brazilian Symposium on Databases (SBBD), 2017.
Capanema, C. G. S., Silva, F. A., and Silva, T. R. d. M. B. Mfa-rnn: Uma rede neural recorrente para predição de próximo local de visita com base em dados esparsos. In Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. SBC, pp. 127–140, 2020.
Coimbra, G. T., Capanema, C. G. S., Silva, F. A., and Silva, T. R. B. Appel: Uma extensão do kepler para enriquecimento de dados geoespaciais. In GEOINFO. pp. 176–181, 2019.
dos Santos, W., Carvalho, L. F., Avelar, G. d. P., Silva, Á., Ponce, L. M., Guedes, D., and Meira, W. Lemonade: A scalable and efficient spark-based platform for data analytics. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID). IEEE, pp. 745–748, 2017.
Hadian, A. and Shahrivari, S. High performance parallel k-means clustering for disk-resident datasets on multi-core cpus. The Journal of Supercomputing 69 (2): 845–863, 2014.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. The weka data mining software: an update. ACM SIGKDD explorations newsletter 11 (1): 10–18, 2009.
Hitachi. Pentaho hitachi vantara. https://www.hitachivantara.com/en-us/products/data-management-analytics/pentaho.html, 2017. Accessed on 11/02/2021. Inc., P. T. Collaborative data science, 2015.
Mapbox. Maps, geocoding, and navigation apis sdks. https://www.mapbox.com/, 2021. Accessed on 18/3/2021.
Microsoft. Azure machine learning. https://azure.microsoft.com/en-us/services/machine-learning/, 2017a. Accessed on 11/2/20121.
Microsoft. Power bi. https://powerbi.microsoft.com/, 2017b. Accessed on 11/2/2021.
Nogueira, T. P., Celes, C. S., Martin, H., Loureiro, A. A., and Andrade, R. M. A statistical method for detecting move, stop, and noise: A case study with bus trajectories. Journal of Information and Data Management 9 (3): 214–214, 2018.
Qlik. Qlik: Data analytics and data integration solutions. https://www.qlik.com/us/, 2017. Accessed on 11/2/2021.
SAS. Sas: Analytics, artificial intelligence and data management software. https://www.sas.com/en_us/home.html, 2017. Accessed on 11/2/2021.
Sisense. Sisense: Business itenlligence (bi), software and analytics tools. https://www.sisense.com/, 2021. Accessed on 11/2/2017.
Statista. Statista: the portal of statistics. https://www.statista.com/statistics/346195/ facebook-global-mobile-dau/, 2017. Accessed on 11/2/2021.
Tableau. Tableau. https://www.tableau.com/, 2017. Accessed on 11/2/2021.
Xavier, W. Z., Xavier, F. H. Z., and Marques-Neto, H. T. Visualizing and analyzing georeferenced workloads of mobile networks. In IEEE International Conference on Pervasive Computing and Communications Workshops. pp. 306–310, 2017.
