Abstract
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.
Highlights
The global ocean plays several critical roles in the physical climate system of the Earth
To address the above challenges, we propose a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata, user behavior, and ontology
The contributions of the proposed system are as follows: (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 data consumers to the appropriate data resources
Summary
The global ocean plays several critical roles in the physical climate system of the Earth. PO.DAAC provides several features to rank the search results, including all-time popularity, monthly popularity, grid spatial resolution, etc This approach largely fails to take account of users’ multidimensional preferences for geospatial data, which often results in less than optimal user experience [10]. The contributions of the proposed system are as follows: (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 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
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