Natural language interfaces (NLIs) provide users with a convenient way to interactively analyze data through natural language queries. Nevertheless, interactive data analysis is a demanding process, especially for novice data analysts. When exploring large and complex datasets from different domains, data analysts do not necessarily have sufficient knowledge about data and application domains. It makes them unable to efficiently elicit a series of queries and extensively derive desirable data insights. In this paper, we develop an NLI with a step-wise query recommendation module to assist users in choosing appropriate next-step exploration actions. The system adopts a data-driven approach to generate step-wise semantically relevant and context-aware query suggestions for application domains of users' interest based on their query logs. Also, the system helps users organize query histories and results into a dashboard to communicate the discovered data insights. With a comparative user study, we show that our system can facilitate a more effective and systematic data analysis process than a baseline without the recommendation module.
翻译:自然语言界面(NLIs)为用户提供了通过自然语言查询进行互动分析数据的方便方式。然而,互动式数据分析是一个要求很高的过程,特别是对于新数据分析员来说。在探索不同领域的大型和复杂数据集时,数据分析员不一定对数据和应用领域有足够的知识。这使得他们无法有效地查询一系列问题并广泛获得理想的数据洞察力。在本文件中,我们开发了一个带有渐进式查询建议模块的NLI,以帮助用户选择适当的下一步探索行动。系统采用了一种数据驱动方法,根据用户的查询日志,为用户感兴趣的应用领域产生渐进式的语义相关和上下文的查询建议。此外,该系统还帮助用户将查询历史和结果组织成一个仪表,以传递发现的数据洞察力。通过比较用户研究,我们显示我们的系统可以促进比没有建议模块的基线更有效和更系统的数据分析进程。