Abstract
Ranked search of datasets has emerged as a need as shared scientific archives grow in size and variety. Our own have shown that IR-style, feature-based relevance scoring can be an effective tool for data discovery in scientific archives. However, maintaining interactive response times as archives scale will be a challenge. We report here on our exploration of performance techniques for Data Near Here, a dataset search service. We present a sample of results evaluating filter-restart techniques in our system, including two variations, adaptive relaxation and contraction. We then outline further directions for research in this domain.
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Maier, D., Megler, V.M., Tufte, K. (2014). Challenges for Dataset Search. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8421. Springer, Cham. https://doi.org/10.1007/978-3-319-05810-8_1
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DOI: https://doi.org/10.1007/978-3-319-05810-8_1
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