Interactive data exploration based on user relevance feedback

Kyriaki Dimitriadou, Olga Papaemmanouil, Y. Diao
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引用次数: 3

Abstract

Interactive Data Exploration (IDE) applications typically involve users that aim to discover interesting objects by it-eratively executing numerous ad-hoc exploration queries. Therefore, IDE can easily become an extremely labor and resource intensive process. To support these applications, we introduce a framework that assists users by automatically navigating them through the data set and allows them to identify relevant objects without formulating data retrieval queries. Our approach relies on user relevance feedback on data samples to model user interests and strategically collects more samples to refine the model while minimizing the user effort. The system leverages decision tree classifiers to generate an effective user model that balances the trade-off between identifying all relevant objects and reducing the size of final returned (relevant and irrelevant) objects. Our preliminary experimental results demonstrate that we can predict linear patterns of user interests (i.e., range queries) with high accuracy while achieving interactive performance.
基于用户相关性反馈的交互式数据探索
交互式数据探索(IDE)应用程序通常涉及用户,这些用户的目标是通过交互式地执行大量特别的探索查询来发现有趣的对象。因此,IDE可以很容易地成为一个极其劳动和资源密集型的过程。为了支持这些应用程序,我们引入了一个框架,该框架通过在数据集中自动导航来帮助用户,并允许他们在不制定数据检索查询的情况下识别相关对象。我们的方法依赖于数据样本上的用户相关性反馈来建模用户兴趣,并有策略地收集更多样本来改进模型,同时最大限度地减少用户的工作量。系统利用决策树分类器生成一个有效的用户模型,该模型在识别所有相关对象和减少最终返回(相关和不相关)对象的大小之间取得平衡。我们的初步实验结果表明,我们可以在实现交互性能的同时高精度地预测用户兴趣的线性模式(即范围查询)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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