We consider the problem of searching scientific data from vast heterogeneous scientific data repositories. This problem is challenging because scientific data contain relatively little text information compared to other search targets such as web pages. On the other hand, the metadata in scientific data contain other characteristic information such as spatio‐temporal information. Although using this information make it possible to improve the search performance, many widely adopted scientific data search engines use this information exclusively for narrowing down search results. In this paper, we propose a novel query generation method using spatial, temporal, and text information based on pseudo relevance feedback. The proposed method generates new spatio‐temporal queries from the initial search results. By using these queries, the search results are reranked such that more related results obtain higher rank. The experimental results show that the proposed method outperforms a baseline method when search targets do not have rich text information. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

As larger and larger amounts of data are harvested, finding just the right piece of information out of this noisy and heterogeneous ocean of data remains challenging. Many widely adopted scientific data search engines continue to be mainly based on text semantics. However, it is not uncommon in scientific big data applications to face collected data that do not possess text information. In this scenario, search engines fail to retrieve potentially relevant data. For instance, even though Pangaea, a digital data library and a publisher for earth system science, contains more than 400,000 datasets, more than 98% lack sufficient text information. In this work, we propose a novel pseudo relevance feedback method based on spatio-temporal and text (STT) information for scientific big data: STT-PRF. Although STT-PRF may simultaneously use STT information, we show that the missing values in space, time or/and the text are handled efficiently. STT-PRF is especially robust even without text information. We tested our STT-PRF method using the Pangaea repository on our Cross-DB Search Platform, which is a search engine for scientific big data based on various latent correlations. Experimental evaluations on such standard metrics as nDCG and Precision/Recall show that STT-PRF outperforms the standard baseline methods.

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