Information exploration tasks are inherently complex, ill-structured, and involve sequences of actions usually spread over many sessions. When exploring a dataset, users tend to experiment higher degrees of uncertainty, mostly raised by knowledge gaps concerning the information sources, the task, and the efficiency of the chosen exploration actions, strategies, and tools in supporting the task solution process. Provided these concerns, exploration tools should be designed with the goal of leveraging the mapping between user's cognitive actions and solution strategies onto the current systems' operations. However, state-of-the-art systems fail in providing an expressive set of operations that covers a wide range of exploration problems. There is not a common understanding of neither which operators are required nor in which ways they can be used by explorers. In order to mitigate these shortcomings, this work presents a formal framework of exploration operations expressive enough to describe at least the majority of state-of-the-art exploration interfaces and tasks. We also show how the framework leveraged a new evaluation approach, where we draw precise comparisons between tools concerning the range of exploration tasks they support.
翻译:在探索数据集时,用户往往会试验高度的不确定性,这主要是由于对信息来源、任务以及所选择的勘探行动、战略和工具在支持任务解决方案进程方面的效率方面的知识差距。如果存在这些关切,则在设计勘探工具时,应着眼于在用户的认知行动和解决方案战略之间对当前系统的运作进行绘图。然而,最先进的系统未能提供一套涵盖广泛勘探问题的明确操作。对于哪些操作者是必需的,探索者可以以何种方式使用这些操作,我们并没有共同的理解。为了减轻这些缺陷,这项工作提出了一个正式的勘探作业框架,足以说明至少大多数最先进的勘探界面和任务。我们还展示了该框架如何利用新的评价方法,在其中我们对其所支持的勘探任务的范围进行精确比较。