Data Stones Data Stones explores the overlap of Chinese and European philosophies offered by data visualisation. A database of every message sent between two people, accumulated through the regular internet usage is transformed into a computer generated rock. These stones are produced procedurally from the mundane dialogue accumulated by the everyday use of instant messaging. I download thousands of messages sent between two people and sort them according to length, date and content (using Latent Dirichlet Allocation). These various means of processing extract patterns and sentiments in what never had any intrinsic order. The stone is treated like a graph, where the statistical patterns my sentiments determines its shape, and becomes an object for contemplation and speculation. Data Stones relying on the logic that if a system encoded a stone, then it can always theoretically be decoded. In contemplating these stones, we hope to crystallise our thoughts, and finds ourselves, staring back.

Do What I Mean, Not What I Say! Design Considerations for Supporting Intent and Context in Analytical Conversation Natural language can be a useful modality for creating and interacting with visualizations but users often have unrealistic expectations about the intelligence of natural language systems. The gulf between user expectations and system capabilities may lead to a disappointing user experience. So xe2x80x94 if we want to engineer a natural language system, what are the requirements around system intelligence? This work takes a retrospective look at how we answered this question in the design of Ask Data, a natural language interaction feature for Tableau. We examine two factors contributing to perceived system intelligence: the systemu0027s ability to understand the analytic intent behind an input utterance and the ability to interpret an utterance contextually (i.e. taking into account the current visualization state and recent actions). Our aim was to understand the ways in which a system would need to support these two aspects of intelligence to enable a positive user experience. We first describe a predesign Wizard of Oz study that offered insight into this question and narrowed the space of designs under consideration. We then reflect on the impact of this study on system development, examining how design implications from the study played out in practice. Our work contributes insights for the design of natural language interaction in visual analytics as well as a reflection on the value of predesign empirical studies in the development of visual analytic systems.