2014/12/01 by Suan Lee, Nam‐Soo Kim, Jinho Kim · 1 citation
Computer Science · #Web Data Mining and Analysis #Advanced Database Systems and Queries #Data Management and Algorithms #Online analytical processing #Computer science #Unstructured data #Data cube #Cube (algebra) #Information retrieval #Data mining #Big data #Data warehouse
paper · doi:10.1109/bdcloud.2014.117
openalex publication_date 2014/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Recently, unstructured data like texts, documents, or SNS messages has been increasingly being used in many applications, rather than structured data consisting of simple numbers or characters. Thus it becomes more important to analysis unstructured text data to extract valuable information for usres decision making. Like OLAP (On-Line Analytical Processing) analysis over structured data, Multi-dimensional analysis for these unstructured data is popularly being required. To facilitate these analysis requirements on the unstructured data, a text cube model on multi-dimensional text database has been proposed. In this paper, we extended the existing text cube model to incorporate TF-IDF (Term Frequency Inverse Document Frequrency) and LM (Language Model) as measurements. Because the proposed text cube model utilizes new measurements which are more popular in information retrieval systems, it is more efficient and effective to analysis text databases. Through experiments, we revealed that the performance and the effectiveness of the proposed text cube outperform the existing one.