Extreme Climate Event Detection Through High Volume of Transactional Consumption Data

Hugo Alatrista-Salas; Mauro León-Payano; Miguel Nunez-del-Prado

doi:10.1007/978-3-030-30278-8_46

Extreme Climate Event Detection Through High Volume of Transactional Consumption Data

Hugo Alatrista-Salas, Mauro León-Payano, Miguel Nunez-del-Prado

Universidad del Pacífico

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Extreme weather events cause irreparable damage to society. At the beginning of 2017, the coast of Peru was hit by the phenomenon called “El Niño Costero”, characterized by heavy rains and floods. According to the United Nations International Strategy for Disasters ISDR, natural disasters comprise a 5-step process. In the last stage - recovery - strategies are aimed at bringing the situation back to normality. However, this step is difficult to achieve if one does not know how the economic sectors have been affected by the extreme event. In this paper, we use two well-known techniques, such as Autoregressive integrated moving average (ARIMA) and Kullback-Leibler divergence to capture a phenomenon and show how the key economic sectors are affected. To do this, we use a large real dataset from banking transactions stored in a Massively Parallel Processing (MPP). Our results show the interest of applying these techniques to better understand the impact of a natural disaster into economic activities in a specific geographical area.

Idioma original	Inglés
Título de la publicación alojada	New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings
Editores	Tatjana Welzer, Vili Podgorelec, Aida Kamišalic Latific, Johann Eder, Robert Wrembel, Mikolaj Morzy, Mirjana Ivanovic, Johann Gamper, Theodoros Tzouramanis, Jérôme Darmont
Lugar de publicación	Cham
Páginas	475-486
Número de páginas	12
ISBN (versión digital)	9783030302771
DOI	https://doi.org/10.1007/978-3-030-30278-8_46
Estado	Publicada - 1 ene. 2019
Evento	Communications in Computer and Information Science - Duración: 1 ene. 2019 → …

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1064
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	Communications in Computer and Information Science
Período	1/01/19 → …

Nota bibliográfica

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Palabras clave

Extreme climate event detection
Parallel processing
Time series
Transactional banking data

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10.1007/978-3-030-30278-8_46

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Alatrista-Salas, H., León-Payano, M., & Nunez-del-Prado, M. (2019). Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. En T. Welzer, V. Podgorelec, A. Kamišalic Latific, J. Eder, R. Wrembel, M. Morzy, M. Ivanovic, J. Gamper, T. Tzouramanis, & J. Darmont (Eds.), New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings (pp. 475-486). (Communications in Computer and Information Science; Vol. 1064).. https://doi.org/10.1007/978-3-030-30278-8_46

Alatrista-Salas, Hugo ; León-Payano, Mauro ; Nunez-del-Prado, Miguel. / Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings. editor / Tatjana Welzer ; Vili Podgorelec ; Aida Kamišalic Latific ; Johann Eder ; Robert Wrembel ; Mikolaj Morzy ; Mirjana Ivanovic ; Johann Gamper ; Theodoros Tzouramanis ; Jérôme Darmont. Cham, 2019. pp. 475-486 (Communications in Computer and Information Science).

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title = "Extreme Climate Event Detection Through High Volume of Transactional Consumption Data",

abstract = "Extreme weather events cause irreparable damage to society. At the beginning of 2017, the coast of Peru was hit by the phenomenon called “El Ni{\~n}o Costero”, characterized by heavy rains and floods. According to the United Nations International Strategy for Disasters ISDR, natural disasters comprise a 5-step process. In the last stage - recovery - strategies are aimed at bringing the situation back to normality. However, this step is difficult to achieve if one does not know how the economic sectors have been affected by the extreme event. In this paper, we use two well-known techniques, such as Autoregressive integrated moving average (ARIMA) and Kullback-Leibler divergence to capture a phenomenon and show how the key economic sectors are affected. To do this, we use a large real dataset from banking transactions stored in a Massively Parallel Processing (MPP). Our results show the interest of applying these techniques to better understand the impact of a natural disaster into economic activities in a specific geographical area.",

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author = "Hugo Alatrista-Salas and Mauro Le{\'o}n-Payano and Miguel Nunez-del-Prado",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Communications in Computer and Information Science ; Conference date: 01-01-2019",

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doi = "10.1007/978-3-030-30278-8_46",

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Alatrista-Salas, H, León-Payano, M & Nunez-del-Prado, M 2019, Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. En T Welzer, V Podgorelec, A Kamišalic Latific, J Eder, R Wrembel, M Morzy, M Ivanovic, J Gamper, T Tzouramanis & J Darmont (eds.), New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings. Communications in Computer and Information Science, vol. 1064, Cham, pp. 475-486, Communications in Computer and Information Science, 1/01/19. https://doi.org/10.1007/978-3-030-30278-8_46

Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. / Alatrista-Salas, Hugo; León-Payano, Mauro; Nunez-del-Prado, Miguel.
New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings. ed. / Tatjana Welzer; Vili Podgorelec; Aida Kamišalic Latific; Johann Eder; Robert Wrembel; Mikolaj Morzy; Mirjana Ivanovic; Johann Gamper; Theodoros Tzouramanis; Jérôme Darmont. Cham, 2019. p. 475-486 (Communications in Computer and Information Science; Vol. 1064).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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N2 - Extreme weather events cause irreparable damage to society. At the beginning of 2017, the coast of Peru was hit by the phenomenon called “El Niño Costero”, characterized by heavy rains and floods. According to the United Nations International Strategy for Disasters ISDR, natural disasters comprise a 5-step process. In the last stage - recovery - strategies are aimed at bringing the situation back to normality. However, this step is difficult to achieve if one does not know how the economic sectors have been affected by the extreme event. In this paper, we use two well-known techniques, such as Autoregressive integrated moving average (ARIMA) and Kullback-Leibler divergence to capture a phenomenon and show how the key economic sectors are affected. To do this, we use a large real dataset from banking transactions stored in a Massively Parallel Processing (MPP). Our results show the interest of applying these techniques to better understand the impact of a natural disaster into economic activities in a specific geographical area.

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Alatrista-Salas H, León-Payano M, Nunez-del-Prado M. Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. En Welzer T, Podgorelec V, Kamišalic Latific A, Eder J, Wrembel R, Morzy M, Ivanovic M, Gamper J, Tzouramanis T, Darmont J, editores, New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings. Cham. 2019. p. 475-486. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-30278-8_46

Extreme Climate Event Detection Through High Volume of Transactional Consumption Data

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Palabras clave

ODS de las Naciones Unidas

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