Textual aggregation approaches in OLAP context: A survey
| Autor: | Yulia A. Strekalova, Mustapha Bouakkaz, Sabine Loudcher, Youcef Ouinten |
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| Přispěvatelé: | Université Amar Telidji - Laghouat, Entrepôts, Représentation et Ingénierie des Connaissances (ERIC), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, University of Florida [Gainesville] (UF) |
| Rok vydání: | 2017 |
| Předmět: |
Structure (mathematical logic)
Decision support system OLAP [SHS.STAT]Humanities and Social Sciences/Methods and statistics Computer Networks and Communications Computer science Online analytical processing Aggregate (data warehouse) Textual data Context (language use) 02 engineering and technology Library and Information Sciences Data science Data warehouse Field (computer science) Aggregation 020204 information systems Similarity (psychology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Information Systems |
| Zdroj: | International Journal of Information Management International Journal of Information Management, Elsevier, 2017, 37 (6), pp.684-69. ⟨10.1016/j.ijinfomgt.2017.06.005⟩ |
| ISSN: | 0268-4012 0143-6236 |
| DOI: | 10.1016/j.ijinfomgt.2017.06.005 |
| Popis: | International audience; In the last decade, OnLine Analytical Processing (OLAP) has taken an increasingly important role as a research field. Solutions, techniques and tools have been provided for both databases and data warehouses to focus mainly on numerical data. however these solutions are not suitable for textual data. Therefore recently, there has been a huge need for new tools and approaches that treat and manipulate textual data and aggregate it as well. Textual aggregation techniques emerge as a key tool to perform textual data analysis in OLAP for decision support systems. This paper aims at providing a structured and comprehensive overview of the literature in the field of OLAP Textual Aggregation. We provide a new classification framework in which the existing textual aggregation approaches are grouped into two main classes, namely approaches based on cube structure and approaches based on text mining. We discuss and synthesize also the potential of textual similarity metrics, and we provide a recent classification of them. |
| Databáze: | OpenAIRE |
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