Top_Keyword: An Aggregation Function for Textual Document OLAP

Ravat, Franck; Teste, Olivier; Tournier, Ronan; Zurfluh, Gilles

doi:10.1007/978-3-540-85836-2_6

Cited by 39 publications

(21 citation statements)

References 9 publications

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“…As a first steps to a more global framework, aggregation functions adapted to textual data were defined. Park et al [52] introduce such functions based on text mining techniques while Ravat et al [67] specify a function based on information retrieval weighing techniques.…”

Section: Detailed Presentation Of Researchesmentioning

confidence: 99%

“…In order to allow aggregation over textual data, adapted aggregation functions are required (such as those suggested in [52]). Recently, an adapted aggregation function based on information retrieval weighing techniques [67] has been proposed. However, as these functions are complex, they still require optimisation.…”

Section: Detailed Presentation Of Researchesmentioning

confidence: 99%

“…1 XML file per Dimension 1 XML file per fact (xFACT) 1 index linking data in each XML file (not described) [65,66,67,68] Specific (ROLAP and XML): conceptual OLAP document analysis model (Galaxy)…”

Section: Article In Pressmentioning

confidence: 99%

“…Finally, the two approaches that offer a more global framework [52,[65][66][67][68] are capable of analysing document-centric XML documents (both can also handle datacentric ones). In these proposals, the analysis capacities, although linked to algebraic operators [66], are still limited.…”

Section: Analysis Framework Comparisonmentioning

confidence: 99%

See 3 more Smart Citations

Finding an application-appropriate model for XML data warehouses

Ravat

Teste

Tournier

et al. 2010

Information Systems

Self Cite

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Section: Detailed Presentation Of Researchesmentioning

confidence: 99%

Section: Detailed Presentation Of Researchesmentioning

confidence: 99%

Section: Article In Pressmentioning

confidence: 99%

Section: Analysis Framework Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Finding an application-appropriate model for XML data warehouses

Ravat

Teste

Tournier

et al. 2010

Information Systems

Self Cite

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“…They consider only external textual documents as sources of data, but they ignore the abundant textual information of attributes in databases. In some studies as [12] and [13], they expose the possibility to pre-process the textual documents with the help of techniques from Data Mining and Information Retrieval, to extract knowledge of these, and to analyze the same in multidimensional specific models. The work of Ravat proposes new measures based on texts, but they do not process the texts contained in the dimensions.…”

Section: Related Workmentioning

confidence: 99%

A new multidimensional model with text dimensions: definition and implementation

Martín-Bautista

Molina

Tejeda

et al. 2013

IJCIS

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We present a new multidimensional model with textual dimensions based on a knowledge structure extracted from the texts, where any textual attribute in a database can be processed, and not only XML texts. This dimension allows to treat the textual data in the same way as the non-textual one in an automatic way, without user's intervention, so all the classical operations in the multidimensional model can been defined for this textual dimension. While most of the models dealing with texts that can be found in the literature are not implemented, in this proposal, the multidimensional model and the OLAP system have been implemented in a software tool, so it can be tested on real data. A case study with medical data is included in this work.

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A Pipeline for Measuring Brand Loyalty Through Social Media Mining

Samoaa

Catania

2021

SOFSEM 2021: Theory and Practice of Computer Science

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Enhancing customer relationships through social media is an area of high relevance for companies. To this aim, Social Business Intelligence (SBI) plays a crucial role by supporting companies in combining corporate data with user-generated content, usually available as textual clips on social media. Unfortunately, SBI research is often constrained by the lack of publicly-available, real-world data for experimental activities. In this paper, we describe our experience in extracting social data and processing them through an enrichment pipeline for brand analysis. As a first step, we collect texts from social media and we annotate them based on predefined metrics for brand analysis, using features such as sentiment and geolocation. Annotations rely on various learning and natural language processing approaches, including deep learning and geographical ontologies. Structured data obtained from the annotation process are then stored in a distributed data warehouse for further analysis. Preliminary results, obtained from the analysis of three well known ICT brands, using data gathered from Twitter, news portals, and Amazon product reviews, show that different evaluation metrics can lead to different outcomes, indicating that no single metric is dominant for all brand analysis use cases.

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Top_Keyword: An Aggregation Function for Textual Document OLAP

Cited by 39 publications

References 9 publications

Finding an application-appropriate model for XML data warehouses

Finding an application-appropriate model for XML data warehouses

A new multidimensional model with text dimensions: definition and implementation

A Pipeline for Measuring Brand Loyalty Through Social Media Mining

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