Analysing personal datasets has traditionally been limited to ‘Quantified Selfers’ who commit significant effort into manually recording and analysing their data. However, the pool of Casual Users (CUs) who can engage with their personal data is increasing due to the prevalence of companies passively collecting user interaction data. In this paper, we execute an online survey exploring what kinds of information users seek about their music listening behaviour. We compare the information needs of CUs to identified Self-Trackers, using music listening as a lens to develop an information space. The paper culminates in a provocation to broaden the audience of personal informatics by updating existing models of interaction to account for casual users, passive data, and episodic reflection.

Distributed tracing tools have emerged in recent years to enable operators of modern internet applications to troubleshoot cross-component problems in deployed applications. Due to the rich, detailed diagnostic data captured by distributed tracing tools, effectively presenting this data is important. However, use of visualisation to enable sensemaking of this complex data in distributed tracing tools has received relatively little attention. Consequently, operators struggle to make effective use of existing tools. In this article we present the first characterisation of distributed tracing visualisation through a qualitative interview study with six practitioners from two large internet companies. Across two rounds of 1-on-1 interviews we use grounded theory coding to establish users, extract concrete use cases and identify shortcomings of existing distributed tracing tools. We derive guidelines for development of future distributed tracing tools and expose several open research problems that have wide reaching implications for visualisation research and other domains.

A common fixture of computer systems research are Visualisation-in-the-Loop Tools: tools that produce complex output data and require a human user to interpret the data visually. However, systems research frequently omits or sidelines details of the visualisation components that were necessary for the tool. In a survey of 1,274 recent systems papers we find that at least 7.7% (98) of them present a visualisation-in-the-loop tool. We also find that the majority of these publications pay no attention and give little explanation to implemented visualisations. We propose that the impact and reach of visualisation-in-the-loop systems research can be greatly enhanced when exposition is given to visualisation, and propose a concrete checklist of steps for authors to realise this opportunity.