SHS Web Conf.
Volume 119, 20213rd International Conference on Quantitative and Qualitative Methods for Social Sciences (QQR’21)
|Number of page(s)||7|
|Section||Technology and Society / Covid-19 Innovations|
|Published online||24 August 2021|
- P.B. Makeshwar, A. Kalra, N.S. Rajput, K.P. Singh, Computational Scalability with Apache Flume and Mahout for Large Scale Round the Clock Analysis of Sensor Network Data, 6, 3 (2015) [Google Scholar]
- A. Hasan, S. Moin, A. Karim, S. Shamshirband, Machine Learning-Based Sentiment Analysis for Twitter Accounts, 15, 3 (2018) [Google Scholar]
- E. Gallinucci, M. Golfarelli, S. Rizzi, Advanced topic modelling for social business intelligence, 20, 14 (2015) [Google Scholar]
- R. Wrembe, Data Warehouses and Olap: Concepts, Architectures and Solutions, 332, 217, December 2006, Paris, France (2006) [Google Scholar]
- R. Berlanga, L. Garcia-Moya, V. Nebot, M.J. Aramburu, I. Sanz, D.M. Llido, SLOD-BI: An Open Data Infrastructure for Enabling Social Business Intelligence, 28, 3 (2015) [Google Scholar]
- I. Paik, T. Golfarelli, H. Tanaka, H. Ohashi, C. Chen, Big data infrastructure for active situation awareness on social network services, 2, 1 (2013) [Google Scholar]
- H. Han, W. Yonggang, C. Tat-Seng, L. Xuelong, Toward Scalable Systems for Big Data Analytics: A Technology Tutorial, 36, 6 (2014) [Google Scholar]
- A. Meier, M. Kaufmann, SQL & NoSQL Databases, Models, Languages, Consistency Options and Architectures for Big Data Management, 218, 169 (2019) [Google Scholar]
- L. Schlesinger, F. Irmert, W. Lehner, Supporting the ETL-process by Web Service technologies, 17, 9 (2005) [Google Scholar]
- F. Laghaei, O. Bin Ibrahim, Using Social Network’s Data By Extraction Transformation Loading (ETL), 11, 4 (2017) [Google Scholar]
- Y. Lu, F. Wang, R. Maciejewski, Business Intelligence from Social Media: A Study from the VAST Box Office Challenge, 11, 5 (2018) [Google Scholar]
- F. Sebastiani, Machine Learning in Automated Text Categorization, 47, 34 (2002) [Google Scholar]
- W. Sherchan, S. Nepal, C. Paris, A Survey of Trust in Social Networks, 33, 19 (2013) [Google Scholar]
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