A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example

• Main Authors: Yongyao Jiang, Yun Li, Chaowei Yang, Fei Hu, Edward M. Armstrong, Thomas Huang, David Moroni, Lewis J. McGibbney, Frank Greguska, Christopher J. Finch
• Format: Article
• Language: English
• Published: MDPI AG 2018-02-01
• Series: ISPRS International Journal of Geo-Information
• Subjects: knowledge base; semantic search; user behavior; metadata; search ranking; recommendation; big data
• Online Access: http://www.mdpi.com/2220-9964/7/2/62

Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system enables semantic query expansion and suggestion to assist users in finding more relevant data; (2) machine-learned ranking is utilized to provide the optimal search ranking based on a number of identified ranking features that can reflect users’ search preferences; (3) a hybrid recommendation module is designed to allow users to discover related data considering metadata attributes and user behavior; (4) an integrated graphic user interface design is developed to quickly and intuitively guide data consumers to the appropriate data resources. As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example. Experiments and a search example show that the proposed system can improve the scientific community’s data search experience by providing query expansion, suggestion, better search ranking, and data recommendation via a user-friendly interface.