research-article

Modern Enterprises in the Bubble: Why Big Data Matters

Authors:

Enrico Giacinto Caldarola,

Antonio Picariello,

Daniela CastellucciaAuthors Info & Claims

ACM SIGSOFT Software Engineering Notes, Volume 40, Issue 1

Pages 1 - 4

https://doi.org/10.1145/2693208.2693228

Published: 06 February 2015 Publication History

Abstract

In the past few years, a massive amount of data has been delivered by an increased and ubiquitous use of Information and Communication Technologies (ICTs) in human activities and a propagation of smart devices or smart sensors, which continuously connect people and things in cyberspace. This huge bubble of data is a gold mine: it is an unlimited source of knowledge and insights about habits and preferences of people and has captured the attention of modern enterprises. Companies look at this data with interest and purpose to gain competitive advantage by applying analytics tools over them. In this context, a new approach is required for mastering data without the risk of ending up in the bubble and collecting a huge meaningless pile of junk data. Starting from a definition of new approaches, this work outlines Big Data strategies for modern enterprises and highlights challenges, emergent solutions and open issues.

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Index Terms

Modern Enterprises in the Bubble: Why Big Data Matters
1. Information systems
  1. Information systems applications

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Published In

cover image ACM SIGSOFT Software Engineering Notes

ACM SIGSOFT Software Engineering Notes Volume 40, Issue 1

January 2015

237 pages

ISSN:0163-5948

DOI:10.1145/2693208

Editor:
Michael Wing

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Copyright © 2015 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 February 2015

Published in SIGSOFT Volume 40, Issue 1

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Cited By

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https://doi.org/10.1109/TKDE.2020.2994521
Kumar AShawkat Ali A(2022)Big Data Visualization Tools, Challenges and Web Search Popularity - An Update till TodayBig Data Intelligence and Computing10.1007/978-981-99-2233-8_22(305-315)Online publication date: 8-Dec-2022
https://dl.acm.org/doi/10.1007/978-981-99-2233-8_22
EKEN S(2020)Büyük Verinin İnteraktif Görselleştirilmesi: Tableau Üzerine Öğrenci DeneyimleriEuropean Journal of Science and Technology10.31590/ejosat.659823(262-271)Online publication date: 1-Apr-2020
https://doi.org/10.31590/ejosat.659823
Divya Zion GTripathy B(2020)Comparative Analysis of Tools for Big Data Visualization and ChallengesData Visualization10.1007/978-981-15-2282-6_3(33-52)Online publication date: 4-Mar-2020
https://doi.org/10.1007/978-981-15-2282-6_3
Muñoz García ALamolle MMartinez-Béjar REspinal Santana A(2019)Learning Ecosystem Ontology with Knowledge Management as a ServiceComputational Collective Intelligence10.1007/978-3-030-28374-2_48(555-567)Online publication date: 4-Sep-2019
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Kozmina NNiedrite LZemnickis J(2018)Information Requirements for Big Data Projects: A Review of State-of-the-Art ApproachesDatabases and Information Systems10.1007/978-3-319-97571-9_8(73-89)Online publication date: 15-Aug-2018
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Castelluccia DCaldarola EBoffoli N(2017)Environmental Big DataACM SIGSOFT Software Engineering Notes10.1145/3011286.301130741:6(1-4)Online publication date: 5-Jan-2017
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Caldarola ERinaldi A(2017)Modelling Multimedia Social Networks Using Semantically Labelled Graphs2017 IEEE International Conference on Information Reuse and Integration (IRI)10.1109/IRI.2017.70(493-500)Online publication date: 4-Aug-2017
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Amirian Pvan Loggerenberg FLang T(2017)Big Data and Big Data TechnologiesBig Data in Healthcare10.1007/978-3-319-62990-2_3(39-58)Online publication date: 12-Sep-2017
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Caldarola EPicariello ARinaldi A(2017)Experiences in WordNet Visualization with Labeled Graph DatabasesKnowledge Discovery, Knowledge Engineering and Knowledge Management10.1007/978-3-319-52758-1_6(80-99)Online publication date: 22-Jan-2017
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