DATA-IMP: An Interactive Approach to Specify Data Imputation Transformations on Large Datasets
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN: 1611-3349, Vol: 13591 LNCS, Page: 55-74
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Conference Paper Description
In recent years, the volume of data to be analyzed has increased tremendously. However, purposeful data analyses on large-scale data require in-depth domain knowledge. A common approach to reduce data volume and preserve interactivity are sampling algorithms. However, when using a sample, the semantic context across the entire dataset is lost, which impedes data preprocessing. In particular data imputation transformations, which aim to fill empty values for more accurate data analyses, suffer from this problem. To cope with this issue, we introduce DATA-IMP, a novel human-in-the-loop approach that enables data imputation transformations in an interactive manner while preserving scalability. We implemented a fully working prototype and conducted a comprehensive user study as well as a comparison to several non-interactive data imputation techniques. We show that our approach significantly outperforms state-of-the-art approaches regarding accuracy as well as preserves user satisfaction and enables domain experts to preprocess large-scale data in an interactive manner.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85140459145&origin=inward; http://dx.doi.org/10.1007/978-3-031-17834-4_4; https://link.springer.com/10.1007/978-3-031-17834-4_4; https://dx.doi.org/10.1007/978-3-031-17834-4_4; https://link.springer.com/chapter/10.1007/978-3-031-17834-4_4
Springer Science and Business Media LLC
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