research-article

OLAP analysis of multidimensional tweet streams for supporting advanced analytics

Authors: Alfredo Cuzzocrea, Carmen De Maio, Giuseppe Fenza, Vincenzo Loia, Mimmo ParenteAuthors Info & Claims

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

Pages 992 - 999

https://doi.org/10.1145/2851613.2851662

Published: 04 April 2016 Publication History

Abstract

In this paper we propose to integrate Time-Aware Fuzzy Formal Concept Analysis theory with OLAP technology over multidimensional tweet streams in order to arrange tweets in the resulting OLAP cube within a suitable hierarchical structure of concepts (i.e., fuzzy lattice), according to their unstructured content. A microblog summarization algorithm is also introduced in order to provide subset of the tweets that best represents data of the OLAP cube according to the analysis perspective. This with the final goal of supporting advanced analytics over social media, which is becoming relevant at now. A detailed real-life and an extensive experimental analysis nicely complete our contributions.

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Cited By

Ben Kraiem MFeki J(2024)Data Warehouse Design to Support Social Media Analysis: The Case of Twitter and FacebookIntelligent Systems Design and Applications10.1007/978-3-031-64779-6_21(218-233)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-64779-6_21
Mallek HGhozzi FGargouri F(2023)Conceptual modeling of big data SPJ operations with Twitter social mediumSocial Network Analysis and Mining10.1007/s13278-023-01112-w13:1Online publication date: 21-Aug-2023
https://doi.org/10.1007/s13278-023-01112-w
Togatorop PSitorus DPurba YTarigan A(2022)Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM)10.1109/ICOSNIKOM56551.2022.10034904(1-6)Online publication date: 19-Oct-2022
https://doi.org/10.1109/ICOSNIKOM56551.2022.10034904
Show More Cited By

Index Terms

OLAP analysis of multidimensional tweet streams for supporting advanced analytics
1. Information systems
  1. Data management systems
    1. Information integration
      1. Data warehouses

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cover image ACM Conferences

SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing

April 2016

2360 pages

ISBN:9781450337397

DOI:10.1145/2851613

Conference Chair:
Sascha Ossowski
University Rey Juan Carlos, Spain

Copyright © 2016 ACM.

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Published: 04 April 2016

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SAC 2016: Symposium on Applied Computing

April 4 - 8, 2016

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SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Ben Kraiem MFeki J(2024)Data Warehouse Design to Support Social Media Analysis: The Case of Twitter and FacebookIntelligent Systems Design and Applications10.1007/978-3-031-64779-6_21(218-233)Online publication date: 25-Jul-2024
https://doi.org/10.1007/978-3-031-64779-6_21
Mallek HGhozzi FGargouri F(2023)Conceptual modeling of big data SPJ operations with Twitter social mediumSocial Network Analysis and Mining10.1007/s13278-023-01112-w13:1Online publication date: 21-Aug-2023
https://doi.org/10.1007/s13278-023-01112-w
Togatorop PSitorus DPurba YTarigan A(2022)Twitter Data Warehouse and Business Intelligence Using Dimensional Model and Data Mining2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM)10.1109/ICOSNIKOM56551.2022.10034904(1-6)Online publication date: 19-Oct-2022
https://doi.org/10.1109/ICOSNIKOM56551.2022.10034904
Moalla INabli AHammami M(2022)Data warehouse building to support opinion analysis in social mediaSocial Network Analysis and Mining10.1007/s13278-022-00960-212:1Online publication date: 1-Sep-2022
https://doi.org/10.1007/s13278-022-00960-2
Cuzzocrea ABringas P(2022)CORE-BCD-mAI: A Composite Framework for Representing, Querying, and Analyzing Big Clinical Data by Means of Multidimensional AI ToolsHybrid Artificial Intelligent Systems10.1007/978-3-031-15471-3_16(175-185)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15471-3_16
Cuzzocrea AFadda EBaldo A(2021)Lyapunov Central Limit Theorem: Theoretical Properties and Applications in Big-Data-Populated Smart City SettingsProceedings of the 2021 5th International Conference on Cloud and Big Data Computing10.1145/3481646.3481652(34-38)Online publication date: 13-Aug-2021
https://dl.acm.org/doi/10.1145/3481646.3481652
Cuzzocrea ACiancarini P(2021)SeDaSOMA: A Framework for Supporting Serendipitous, Data-As-A-Service-Oriented, Open Big Data Management and AnalyticsProceedings of the 2021 5th International Conference on Cloud and Big Data Computing10.1145/3481646.3481647(1-7)Online publication date: 13-Aug-2021
https://dl.acm.org/doi/10.1145/3481646.3481647
Wang THarvey JLeung CPazdor AChauhan AFan LCuzzocrea A(2021)Distributed Big Data Computing for Supporting Predictive Analytics of Service Requests2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00257(1723-1728)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00257
Fitters WCuzzocrea AHassani M(2021)Enhancing LSTM Prediction of Vehicle Traffic Flow Data via Outlier Correlations2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00039(210-217)Online publication date: Jul-2021
https://doi.org/10.1109/COMPSAC51774.2021.00039
Cuzzocrea A(2021)Big Data Lakes: Models, Frameworks, and Techniques2021 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp51126.2021.00010(1-4)Online publication date: Jan-2021
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