Quality management in social business intelligence projects
![Thumbnail](/xmlui/bitstream/handle/10234/205479/76698_AramburuMJ_2021.pdf.jpg?sequence=5&isAllowed=y)
View/ Open
Impact
![Google Scholar](/xmlui/themes/Mirage2/images/uji/logo_google.png)
![Microsoft Academico](/xmlui/themes/Mirage2/images/uji/logo_microsoft.png)
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/146069
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
Quality management in social business intelligence projectsDate
2021Publisher
SciTePress Science and Technology PublicationsISBN
9789897585098ISSN
2184-4992Bibliographic citation
Aramburu, M.; Berlanga, R. and Lanza-Cruz, I. (2021). Quality Management in Social Business Intelligence Projects. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 320-327Type
info:eu-repo/semantics/conferenceObjectVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
Social networks have become a new source of useful information for companies. Increasing the value of social data requires, first, assessing and improving the quality of the relevant data and, subsequently, developing ... [+]
Social networks have become a new source of useful information for companies. Increasing the value of social data requires, first, assessing and improving the quality of the relevant data and, subsequently, developing practical solutions that apply them in business intelligence tasks. This paper focuses on the Twitter social network and the processing of social data for business intelligence projects. With this purpose, the paper starts by defining the special requirements of the analysis cubes of a Social Business Intelligence (SoBI) project and by reviewing previous work to demonstrate the lack of valid approaches to this problem. Afterwards, we present a new data processing method for SoBI projects whose main contribution is a phase of data exploration and profiling that serves to build a quality data collection with respect to the analysis objectives of the project. [-]
Is part of
Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1Funder Name
Ministerio de Industria y Comercio | Universitat Jaume I
Project code
TIN2017-88805-R | PREDOC/2017/28
Rights
info:eu-repo/semantics/openAccess