ABSTRACT

With the growing focus on business analytics and data-driven decision-making, there is a greater need for humans to interact effectively with data. We propose that presenting data to human users in terms of instances and attributes provides a more flexible and usable structure for querying, exploring, and analysing data. Compared to a traditional representation, an instance-based representation does not impose any predefined classification schema over the data when it is presented to users. This paper examines the potential utility of instance-based data through two laboratory experiments – the first focusing on exploration of data for pattern discovery (open-ended tasks) and the second on retrieval of information (closed-ended tasks). In both cases, participants were able to achieve better results in tasks using instance-based data than using class-based representations. Given the growing need for self-service analytics, as well as using information for purposes not anticipated when it was collected, we show that instance-based representations can be an effective way to satisfy the emerging needs of information users.

摘要

随着对业务分析和数据驱动决策的日益关注，人类更需要与数据进行有效交互。我们建议根据实例和属性向人类用户呈现数据，为查询、探索和分析数据提供更灵活和可用的结构。与传统表示相比，基于实例的表示在呈现给用户时不会对数据强加任何预定义的分类模式。本文通过两个实验室实验检验了基于实例的数据的潜在效用——第一个侧重于探索数据以进行模式发现（开放式任务），第二个侧重于信息检索（封闭式任务）。在这两种情况下，与使用基于类的表示相比，参与者能够在使用基于实例的数据的任务中获得更好的结果。鉴于对自助分析的需求不断增长，以及将信息用于收集时未预料到的目的，我们表明基于实例的表示可以成为满足信息用户新兴需求的有效方式。