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 - http://www.scopus.com/inward/record.url?scp=85072946050&partnerID=8YFLogxK
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 -