Warehousing and analysing textual data

Abstract

Traditional data warehousing technologies have been widely used for simple data analysis. Nonetheless, they are not suitable for textual data. To deal with this type of complex data, new systems have been proposed. However, they only cover partially the complexity of textual data, which might therefore affect decision-making quality. This paper proposes an efficient approach for warehousing and analysing textual data. To tackle the main challenges facing these data, three major contributions are presented throughout our manuscript: i) a Semantic Text Cube Model (ST-Cube); ii) a new ETL approach (Extract Transform and Load); iii) an OLAP aggregation operator called Top_KRankedTopics. For the validation of our approach, we have developed and implemented a platform for the storage and the analysis of text documents. Experimental results show that our approach improves significantly the quality of information extracted from textual documents, which prove the effectiveness of our approach for textual data analysis.