EXODuS: Exploratory OLAP over Document Stores
作者：
Highlights：
• The growing use of document stores has resulted in large document collections holding precious information, which could be profitably integrated into existing BI systems.
• Due to their schemaless nature, document stores are hardly accessible via direct OLAP querying.
• We propose an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP.
• To discover multidimensional hierarchies despite the lack of structure, we adopt a data-driven approach based on the incremental detection of approximate FDs.
• An experimental evaluation on three real-world datasets shows the efficiency of our approach.
摘要
•The growing use of document stores has resulted in large document collections holding precious information, which could be profitably integrated into existing BI systems.•Due to their schemaless nature, document stores are hardly accessible via direct OLAP querying.•We propose an interactive, schema-on-read approach to enable OLAP querying of document stores in the context of self-service BI and exploratory OLAP.•To discover multidimensional hierarchies despite the lack of structure, we adopt a data-driven approach based on the incremental detection of approximate FDs.•An experimental evaluation on three real-world datasets shows the efficiency of our approach.
论文关键词：Document stores,JSON,Exploratory OLAP,Self-service BI,Multidimensional modeling
论文评审过程：Received 7 July 2017, Revised 28 September 2017, Accepted 19 November 2017, Available online 21 November 2017, Version of Record 5 November 2018.
论文官网地址：https://doi.org/10.1016/j.is.2017.11.004
