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International audience; Abstract Text mining approaches are commonly used to discover relevant information and relationships in hugeamounts of text data. The term data mining refers to methods for analyzing data with the objective of finding patternsthat aggregate the main properties of the data. The merger between the data mining approaches and on-line analyticalprocessing (OLAP) tools allows us to refine techniques used in textual aggregation. In this paper, we propose a novel aggregation function for textual data based on the discovery of frequent closed patterns in a generated documents/keywords matrix. Our contribution aims at using a data mining technique, mainly a closed pattern mining algorithm, to aggregate keywords. An experimental study on areal corpus of more than 700 scientific papers collected on Microsoft Academic Search shows that the proposed algorithm largely outperforms four state-of-the-art textual aggregation methods in terms of recall, precision, F-measure and runtime.

International audience; We present in this paper a system for textual aggregation from scientific documents in the online analytical processing (OLAP) context. The system extracts keywords automatically from a set of documents according to the lists compiled in the Microsoft Academia Search web site. It gives the user the possibility to choose their methods of aggregation among the implemented ones. That is TOP-Keywords, TOPIC, TUBE, TAG, BienCube and GOTA. The performance of the chosen methods, in terms of recall, precision, F-measure and runtime, is investigated with two real corpora ITINNOVATION and OHSUMED with 600 and 13,000 scientific articles respectively, other corpora can be integrated to the system by users.

International audience; Data warehousing and On-Line Analytical Processing (OLAP) are essential elements to decision support. In the case of textual data, decision support requires new tools, mainly textual aggregation functions, for better and faster high level analysis and decision making. Such tools will provide textual measures to users who wish to analyse documents online. In this paper, we propose a new aggregation function for textual data in an OLAP context based on the K-means method. This approach will highlight aggregates semantically richer than those provided by classical OLAP operators. The distance used in K-means is replaced by the Google similarity distance which takes into account the semantic similarity of keywords for their aggregation. The performance of our approach is analyzed and compared to other methods such as Topkeywords, TOPIC, TuBE and BienCube. The experimental study shows that our approach achieves better performances in terms of recall, precision,F-measure complexity and runtime.

International audience; With the tremendous growth of unstructured data in the Business Intelligence, there is a need for incorporating textual data into data warehouses, to provide an appropriate multidimensional analysis (OLAP) and develop new approaches that take into account the textual content of data. This will provide textual measures to users who wish to analyse documents online. In this paper, we propose a new aggregation function for textual data in an OLAP context. For aggregating keywords, our contribution is to use a data mining technique, such as kmeans, but with a distance based on the Google similarity distance. Thus our approach considers the semantic similarity of keywords for their aggregation. The performance of our approach is analyzed and compared to another method using the k-bisecting clustering algorithm and based on the Jensen-Shannon divergence for the probability distributions. The experimental study shows that our approach achieves better performances in terms of recall, precision,F-measure complexity and runtime.