Custom application programming interface data extractor applied to the Klarna e-commerce dataset

Anas El Attaoui, Alaeddine Boukhalfa, Sara Rhouas, Norelislam El Hami


The use of smart technologies, the internet of things (IoT), social media, and others produce a billion or more pieces of data in different formats. Big data has risen to become the most sought-after field in computer science. The e-commerce evolved significantly and continued to flow until now and even after the pandemic. So, big data technologies helped with the development and approach to collecting, storing, processing, and extracting the data in this field. This paper proposes an application programming interface (API) data extractor tool applied to a collection of e-commerce public websites named “Klarna dataset” to extract its data, and an analysis of the results. The study of e-commerce sales has given results matching universal e-commerce sales tendencies. The peak of the number of e-commerce transactions and sales was between 2018-2019. Thus, the highest e-commerce sales price was in the United States for “luxury” or “fancy” products, and the highest sales in Europe were in Frankfurt, Germany, for hardware and gaming material.


Big data; Business intelligence; Data analysis; E-commerce sales; Non structured data; Webpage dataset

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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