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
Publisher	Springer Verlag
Pages	475-486
Number of pages	12
ISBN (Print)	9783030302771
DOIs	https://doi.org/10.1007/978-3-030-30278-8_46
State	Published - 2019
Externally published	Yes
Event	Workshops and doctoral consortium papers of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019 - Bled, Slovenia Duration: 8 Sep 2019 → 11 Sep 2019

Publication series

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

Conference

Conference	Workshops and doctoral consortium papers of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019
Country/Territory	Slovenia
City	Bled
Period	8/09/19 → 11/09/19

UN SDGs

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

SDG 13 Climate Action

Keywords

Extreme climate event detection
Parallel processing
Time series
Transactional banking data

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). Springer Verlag. 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. Springer Verlag, 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.",

keywords = "Extreme climate event detection, Parallel processing, Time series, Transactional banking data",

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

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Workshops and doctoral consortium papers of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019 ; Conference date: 08-09-2019 Through 11-09-2019",

year = "2019",

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

language = "English",

isbn = "9783030302771",

series = "Communications in Computer and Information Science",

publisher = "Springer Verlag",

pages = "475--486",

editor = "Tatjana Welzer and Vili Podgorelec and \{Kami{\v s}alic Latific\}, Aida and Johann Eder and Robert Wrembel and Mikolaj Morzy and Mirjana Ivanovic and Johann Gamper and Theodoros Tzouramanis and J{\'e}r{\^o}me Darmont",

booktitle = "New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings",

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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, Springer Verlag, pp. 475-486, Workshops and doctoral consortium papers of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019, Bled, Slovenia, 8/09/19. 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. Springer Verlag, 2019. p. 475-486 (Communications in Computer and Information Science; Vol. 1064).

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

TY - GEN

T1 - Extreme Climate Event Detection Through High Volume of Transactional Consumption Data

AU - Alatrista-Salas, Hugo

AU - León-Payano, Mauro

AU - Nunez-del-Prado, Miguel

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2019

Y1 - 2019

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.

AB - 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.

KW - Extreme climate event detection

KW - Parallel processing

KW - Time series

KW - Transactional banking data

UR - https://www.scopus.com/pages/publications/85072946050

U2 - 10.1007/978-3-030-30278-8_46

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

M3 - Conference contribution

AN - SCOPUS:85072946050

SN - 9783030302771

T3 - Communications in Computer and Information Science

SP - 475

EP - 486

BT - New Trends in Databases and Information Systems - ADBIS 2019 Short Papers, Workshops BBIGAP, QAUCA, SemBDM, SIMPDA, M2P, MADEISD, and Doctoral Consortium 2019, Proceedings

A2 - Welzer, Tatjana

A2 - Podgorelec, Vili

A2 - Kamišalic Latific, Aida

A2 - Eder, Johann

A2 - Wrembel, Robert

A2 - Morzy, Mikolaj

A2 - Ivanovic, Mirjana

A2 - Gamper, Johann

A2 - Tzouramanis, Theodoros

A2 - Darmont, Jérôme

PB - Springer Verlag

T2 - Workshops and doctoral consortium papers of the 23rd European Conference on Advances in Databases and Information Systems, ADBIS 2019

Y2 - 8 September 2019 through 11 September 2019

ER -

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. Springer Verlag. 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

UN SDGs

Keywords

Access to Document

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