research-article

Meta-stars: multidimensional modeling for social business intelligence

Authors:

Enrico Gallinucci,

Matteo Golfarelli,

Stefano RizziAuthors Info & Claims

DOLAP '13: Proceedings of the sixteenth international workshop on Data warehousing and OLAP

Pages 11 - 18

https://doi.org/10.1145/2513190.2513195

Published: 28 October 2013 Publication History

Abstract

Social business intelligence is the discipline of combining corporate data with user-generated content (UGC) to let decision-makers improve their business based on the trends perceived from the environment. A key role in the analysis of textual UGC is played by topics, meant as specific concepts of interest within a subject area. To enable aggregations of topics at different levels, a topic hierarchy is to be defined. Some attempts have been made to address some of the peculiarities of topic hierarchies, but no comprehensive solution has been found so far. The approach we propose to model topic hierarchies in ROLAP systems is called meta-stars. Its basic idea is to use meta-modeling coupled with navigation tables and with traditional dimension tables: navigation tables support hierarchy instances with different lengths and with non-leaf facts, and allow different roll-up semantics to be explicitly annotated; meta-modeling enables hierarchy heterogeneity and dynamics to be accommodated; dimension tables are easily integrated with standard business hierarchies. After outlining a reference architecture for social business intelligence and describing the meta-star approach, we discuss its effectiveness and efficiency by showing its querying expressiveness and by presenting some experimental results for query performances.

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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
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
Walha AGhozzi FGargouri F(2021)Design and Execution of ETL Process to Build Topic Dimension from User-Generated ContentResearch Challenges in Information Science10.1007/978-3-030-75018-3_25(374-389)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75018-3_25
Show More Cited By

Index Terms

Meta-stars: multidimensional modeling for social business intelligence
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Data management systems
    1. Database design and models
  2. Information systems applications
    1. Decision support systems

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cover image ACM Conferences

DOLAP '13: Proceedings of the sixteenth international workshop on Data warehousing and OLAP

October 2013

110 pages

ISBN:9781450324120

DOI:10.1145/2513190

General Chair:
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Ladjel Bellatreche
ISAE-ENSMA, France
,
Alfredo Cuzzocrea
ICAR-CNR and University of Calabria, Italy

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 28 October 2013

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CIKM'13

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 28, 2013

California, San Francisco, USA

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DOLAP '13 Paper Acceptance Rate 13 of 26 submissions, 50%;

Overall Acceptance Rate 29 of 79 submissions, 37%

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CIKM '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
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
Walha AGhozzi FGargouri F(2021)Design and Execution of ETL Process to Build Topic Dimension from User-Generated ContentResearch Challenges in Information Science10.1007/978-3-030-75018-3_25(374-389)Online publication date: 8-May-2021
https://doi.org/10.1007/978-3-030-75018-3_25
Gallinucci EGolfarelli MRizzi S(2018)Advanced topic modeling for social business intelligenceInformation Systems10.1016/j.is.2015.04.00553:C(87-106)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1016/j.is.2015.04.005
Rizzi S(2018)Business IntelligenceEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_881(363-368)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_881
Moalla INabli AHammami M(2017)Integration of a multidimensional schema from different social media to analyze customers'opinions2017 11th International Conference on Research Challenges in Information Science (RCIS)10.1109/RCIS.2017.7956564(391-400)Online publication date: May-2017
https://doi.org/10.1109/RCIS.2017.7956564
Dabbechi HHaddar NAbdallah MHaddar K(2017)A Unified Multidimensional Data Model from Social Networks for Unstructured Data Analysis2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)10.1109/AICCSA.2017.70(415-422)Online publication date: Oct-2017
https://doi.org/10.1109/AICCSA.2017.70
Moalla INabli ABouzguenda LHammami M(2017)Data warehouse design approaches from social media: review and comparisonSocial Network Analysis and Mining10.1007/s13278-017-0423-87:1Online publication date: 7-Feb-2017
https://doi.org/10.1007/s13278-017-0423-8
Yangui RNabli AGargouri F(2017)Community Detection-Based Methodology to Data Warehouse Modeling from Social Network: Application to Handicraft Women Social NetworkSocial Interactions and Networking in Cyber Society10.1007/978-981-10-4190-7_10(109-123)Online publication date: 30-May-2017
https://doi.org/10.1007/978-981-10-4190-7_10
Moedeen BJeerooburkhan A(2016)Evaluating the strategic role of Social Media Analytics to gain business intelligence in Higher Education Institutions2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies (EmergiTech)10.1109/EmergiTech.2016.7737357(303-308)Online publication date: Aug-2016
https://doi.org/10.1109/EmergiTech.2016.7737357
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