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A Tool for Study on Impact of Big Data Technologies on Firm Performance

Lotfi Chaimaa, Srinivasan Swetha, Ertz Myriam et Latrous Imen. (2022). A Tool for Study on Impact of Big Data Technologies on Firm Performance. Dans Intelligent Communication Technologies and Virtual Mobile Networks. (131, p. 501-515). Lecture Notes on Data Engineering and Communications Technologies. Singapore : Springer.

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Organizations can use big data analytics to evaluate large data volumes and collect new information. It aids in answering basic inquiries concerning business operations and performance. It also aids in the discovery of unknown patterns in massive datasets or combinations of datasets. Overall, companies use big data in their systems to enhance operations, provide better customer service, generate targeted marketing campaigns, and take other activities that can raise revenue and profitability in the long run. Therefore, it’s becoming increasingly important to apply and analyze big data approaches for business growth in today’s data-driven world. More precisely, given the abundance of data available on the Internet, whether via social media, websites, online portals, or platforms, to mention a few, businesses must understand how to mine that data for meaningful insights. In this context, Web scraping is an essential strategy. As a result, this work aims to explain the application of the developed tool to the specific case of retrieving big data information about the particular companies in our sample. The paper starts with a short literature review about Web scraping then discusses the tools and methods utilized, describing how the developed technology was applied to the specific scenario of retrieving information about big data usage in the enterprises present in our sample.

Type de document:Chapitre de livre
Lieu de publication:Singapore
Identifiant unique:10.1007/978-981-19-1844-5_40
Sujets:Sciences sociales et humaines > Sciences de la gestion > Management
Sciences naturelles et génie > Sciences mathématiques > Informatique
Département, module, service et unité de recherche:Départements et modules > Département des sciences économiques et administratives
Mots-clés:big data, web scraping, text mining, data, Internet, social media, information, business, données de masse, extraction de données, médias sociaux, affaires
Déposé le:11 févr. 2023 20:11
Dernière modification:20 juill. 2023 04:00
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