ETL4Social-Data: Modeling Approach for Topic Hierarchy

Afef Walha, Faiza Ghozzi, Faïez Gargouri

Abstract

Transforming social media data into meaningful and useful information to enable more effective decision-making is nowadays a hot topic for Social Business Intelligence (SBI) systems. Integrating such data into Social Data Warehouse (SDW) is in charge of ETL (Extraction, Transformation and Loading) which are the typical processes recognized as a complex combination of operations and technologies that consumes a significant portion of the DW development efforts. These processes become more complex when we consider the unstructured social sources. For that, we propose an ETL4Social modeling approach that designs ETL processes suitable to social data characteristics. This approach offers specific models to social ETL operations that help ETL designer to integrate data. A key role in the analysis of textual data is also played by topics, meant as specific concepts of interest within a subject area. In this paper, we mainly insist on emerging topic discovering models from textual media clips. The proposed models are instantiated through Twitter case study. ETL4Social is considered a standard-based modeling approach using Business Process Modeling and Notation (BPMN). ETL Operations models are validated based on ETL4Social meta-model, which is an extension of BPMN meta-model.

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Paper Citation


in Harvard Style

Walha A., Ghozzi F. and Gargouri F. (2017). ETL4Social-Data: Modeling Approach for Topic Hierarchy.In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD, ISBN 978-989-758-272-1, pages 107-118. DOI: 10.5220/0006588901070118


in Bibtex Style

@conference{keod17,
author={Afef Walha and Faiza Ghozzi and Faïez Gargouri},
title={ETL4Social-Data: Modeling Approach for Topic Hierarchy},
booktitle={Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,},
year={2017},
pages={107-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006588901070118},
isbn={978-989-758-272-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 2: KEOD,
TI - ETL4Social-Data: Modeling Approach for Topic Hierarchy
SN - 978-989-758-272-1
AU - Walha A.
AU - Ghozzi F.
AU - Gargouri F.
PY - 2017
SP - 107
EP - 118
DO - 10.5220/0006588901070118