Textual aggregation approaches in OLAP context: A survey |
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| Institution: | 1. LIM Laboratory, University of Laghouat, Algeria;2. ERIC Laboratory, University of Lyon 2, France;3. JC College, University of Florida, USA;1. Independent Scholar, Logistics Management Expert, Menemen, Izmir, Turkey;2. Department of Management, Istanbul Commerce University, Istanbul, Turkey;3. Business Administration, American University of the Middle East, Kuwait;4. Department of Industrial Engineering, Istanbul Technical University, Istanbul, Turkey;5. Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK, USA;1. School of Business, Jiangnan University, China;2. Research Institute of Smart Senior Care, School of Information, Renmin University of China, China;3. School of Information Resources Management, Renmin University of China, China;1. Gerald D. Hines College of Architecture and Design, University of Houston, 4200 Elgin St., Houston, TX 77204-4000, USA;2. I3B, Universitat Politècnica de València, Camino de Vera s/n, 46010 Valencia, Spain;3. Institute of New Imaging Technologies, Universitat Jaume I, Castellón, Spain;1. Yeungnam University, Gyeongsan, South Korea;2. University of North Texas, Denton, TX, USA;1. University of North Carolina at Chapel Hill, 200 Manning Hall, Chapel Hill, NC 27599-3360, USA;2. Syracuse University, Hinds Hall 348, Syracuse, NY 13244-4100, USA;3. Kateryna Bondar International University of La Rioja Avenida de la Paz, 137, 26006, Logroño, Spain;4. Formerly of the Center for Technology and Innovation Management, CeTIM@ UniBw München, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany |
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| Abstract: | 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. |
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| Keywords: | Aggregation Data warehouse OLAP Textual data Data mining |
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