2010/01/01 by Bolin Ding, Bo Zhao, Cindy Xide Lin +2 · 2 citations
Computer Science · Mathematics · #Data Management and Algorithms #Advanced Database Systems and Queries #Caching and Content Delivery #Computer science #Information retrieval #Ranking (information retrieval) #Cube (algebra) #Tuple #Dimension (graph theory) #Relevance (law) #Data cube #Matching (statistics) #Set (abstract data type) #Online analytical processing #Keyword search #Rank (graph theory) #Data mining #Data warehouse #Combinatorics #Mathematics #Discrete mathematics
paper · doi:10.1109/icde.2010.5447838
openalex publication_date 2010/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Previous studies on supporting keyword queries in RDBMSs provide users with a ranked list of relevant linked structures (e.g. joined tuples) or individual tuples. In this paper, we aim to support keyword search in a data cube with text-rich dimension(s) (so-called text cube). Each document is associated with structural dimensions. A cell in the text cube aggregates a set of documents with matching dimension values on a subset of dimensions. Given a keyword query, our goal is to find the top-k most relevant cells in the text cube. We propose a relevance scoring model and efficient ranking algorithms. Experiments are conducted to verify their efficiency.