Data is everywhere. The amount of collected and generated data every year exceeds the amount of all years before (McAfee et al., 2012). Through the pervasiveness of information and communication technology, data is created in almost all commercial and non- commercial processes, whether known by the user or not. Every step leaves a digital footprint, ready to be processed. But not only the amount of data is unprecedented. According to McAfee et al. (2012) three V’s characterize what Big Data is. Volume as we have already is the most obvious driver of a need for new methods to handle Big Data. Velocity refers to the speed of how fast data is generated and how fast it needs to be processed. Real-time stock data needs evaluation now. Lastly, variety implies that data comes in various (un-)structured formats, but is at the same time heavily interconnected. Mobile phone users not only generate data from surfing the web, but also generate location data, app data, and health data. Sensors are ubiquitous and their data needs to be integrated. In face of the Internet of Things, Industrial Internet, or Industry 4.0 and increasingly pervasive digitalization a tsunami of data will revolutionize our everyday lives. Can we use Big Data to tackle global challenges?

Various approaches have been developed to grasp Big Data. Machine Learning addresses Big Data by using algorithmic approaches to tackle the sheer size and complexity of data. On the other hand, Visual Analytics is a field that tries to combine information visualization – the science of visually displaying quantitative information – with nearby fields, such as knowledge discovery, cognitive and perceptual sciences, statistical analysis. Bringing those two approaches together is the aim of Human-Computer Interaction and Knowledge Discovery in Databases (HCI-KDD, cf. Holzinger, 2013). The overall aim is to support decision-making on the basis of data. Or, how do we get from large amounts of data from the digital world into actionable knowledge in the mental world?


Many of the hard questions have been approached, yet remain partially unanswered. Where is the place of the Human in the Loop (Holzinger 2016)? How do we design interfaces that support the users in making decisions (Sedlmair et al. 2012)? How do we technically create visualizations that represent hard scientific problems (Childs et al. 2013)? How much does a visualization tool need to be tailored the specific problem, how much generalization is possible (Wickham, 2010)? What insights can be drawn from a specific visualization, and by whom (Yi et al. 2008)?


The aim of the HFIDSS workshop is to identify a research agenda for the intersection of Big Data, human-computer interaction and information visualization. What are the most pressing research topics?