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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 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.

Original language	English
Title of host publication	New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings
Editors	Tatjana Welzer, Vili Podgorelec, Aida Kamišalic Latific, Johann Eder, Robert Wrembel, Mikolaj Morzy, Mirjana Ivanovic, Johann Gamper, Theodoros Tzouramanis, Jérôme Darmont
Place of Publication	Cham
Pages	475-486
Number of pages	12
ISBN (Electronic)	9783030302771
DOIs	https://doi.org/10.1007/978-3-030-30278-8_46
State	Published - 1 Jan 2019
Event	Communications in Computer and Information Science - Duration: 1 Jan 2019 → …

Publication series

Name	Communications in Computer and Information Science
Volume	1064
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	Communications in Computer and Information Science
Period	1/01/19 → …

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Extreme climate event detection
Parallel processing
Time series
Transactional banking data

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

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

Cite this

Alatrista-Salas, H., León-Payano, M., & Nunez-del-Prado, M. (2019). Extreme Climate Event Detection Through High Volume of Transactional Consumption Data. In 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",

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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. In Welzer T, Podgorelec V, Kamišalic Latific A, Eder J, Wrembel R, Morzy M, Ivanovic M, Gamper J, Tzouramanis T, Darmont J, editors, 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

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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