Knowing how to construct text-based Search Queries (SQs) for use in Search Engines (SEs) such as Google or Wikipedia has become a fundamental skill. Though much data are available through such SEs, most structured datasets live outside their scope. Visualization tools aid in this limitation, but no such tools come close to the sheer amount of information available through general-purpose SEs. To fill this gap, this paper presents Q4EDA, a novel framework that converts users' visual selection queries executed on top of time series visual representations, providing valid and stable SQs to be used in general-purpose SEs and suggestions of related information. The usefulness of Q4EDA is presented and validated by users through an application linking a Gapminder's line-chart replica with a SE populated with Wikipedia documents, showing how Q4EDA supports and enhances exploratory analysis of United Nations world indicators. Despite some limitations, Q4EDA is unique in its proposal and represents a real advance towards providing solutions for querying textual information based on user interactions with visual representations.
翻译:了解如何构建用于谷歌或维基百科等搜索引擎的基于文本的搜索查询(SQ)已成为一项基本技能。虽然通过这些搜索引擎可以获取大量数据,但大多数结构化数据集都在其范围之外。视觉化工具在这一限制下提供了帮助,但没有任何此类工具接近于通过通用Ses提供的大量信息。为填补这一空白,本文件介绍了Q4EDA,这是一个新颖的框架,它转换了在时间序列直观演示之外执行的用户视觉选择询问,提供了用于一般用途SE的有效和稳定的SQ,并提出了相关信息的建议。Q4EDA的有用性由用户通过将Gapminder的直线图复制与带有维基百科文件的SEpik文档连接起来的应用程序加以展示和验证,表明Q4EDA如何支持和加强对联合国世界指标的探索性分析。尽管存在一些局限性,Q4EDA在其提案中是独一无二的,它代表了在提供基于用户与直观演示的互动的文本信息的解决方案方面取得真正的进展。