Human Factors in Information Visualization and Decision Support Systems
Venue: Mensch Computer Workshopband

With the increase in data availability and data volume it becomes increasingly important to extract information and actionable knowledge from data. Information Visualization helps the user to understand data by utilizing vision as a relatively parallel input channel to the user's mind. Decision Support systems on the other hand help users in making information actionable, by suggesting beneficial decisions and presenting them in context. Both fields share a common need for understanding the interface between the computer and the human. This makes human factors research critical for both fields. Understanding limitations of human perception, cognition and action, as well as their variance must be understood to fully leverage information visualization and decision support. This article reflects on research agendas for investigating human factors in the aforementioned fields. 1 Too much data, too little time 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 noncommercial 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. Veroffentlicht durch die Gesellschaft fur Informatik e.V. 2016 in B. Weyers, A. Dittmar (Hrsg. Mensch und Computer 2016 Workshopbeitrage, 4. 7. September 2016, Aachen.  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) This article reflects on these questions and its implications and elaborates on the meaning of human factors research for these questions. It furthermore refers to articles with research agendas in their given field that elaborate on these questions. 2 Managing Information Complexity Managing large amounts of data can be a critical advantage for business in the age of Industrie 4.0 and the Internet of Things. Corporate information management or Enterprise Resource Planning (ERP) systems help decision makers to steer business externally and internally. These systems may encapsulate and aggregate key performance indicators into actionable knowledge or helpful information. Still, these systems rely on machine learning or data analysis techniques not immune to faulty outcomes. When data, algorithm output and personal knowledge are in dissonance with reality bad decisions can be made. How individuals deal with this dissonance do they favor their knowledge over seemingly perfect algorithms is highly individual and thus of interest to human factors research. What conditions are necessary for decision makers to adequately judge the information from an ERP System must be understood, before automated decision making or monitoring of cyberphysical systems may become widely accepted. The algorithm human hybrid decisionmaking depends on understanding interactions of system, user and context (Brauner et al. 2016) Human Factors in Information Visualization and Decision Support Systems 3 Understanding these interactions is not only necessary when steering a corporation, but also when trying to automatize analysis of business critical data from text sources. Text mining approaches have come a long way; yet, they still rely on human feedback to adequately map topics from documents in new domains. Integrating this feedback into the algorithm can be used to understand research output in a digitalized organization to maintain a competitive advantage in technology forecasting, for instance.  Visualizing the output of such algorithms is mostly done using graph based approaches, visualizing topic document relations or co authorship networks. Various tools exist that help understand the content and social structure encoded in digital libraries. But graph representations, as ingenious they are, come with a sensitive complexity functions. Networks of a given size can exponentially increase in complexity given not only their relations but also their communities. Understanding graph complexity is necessary to help design informative graph based visualizations that are rich in information, but also understandable (Abels et al. 2016) 3 Medical Applications The largest field of application of Big Data approaches is medicine. Medicine suffers from quantity and the unstructured nature of its data, often aggregated from different sensors, with different timings and various levels of aggregations. Medical data is highly patientdependent as no patient is alike. Detecting anomalies from image data or time series data is hard for humans, thus the large field of biomedical data mining. Still, in the end the doctor has to make a decision based on actionable knowledge. Doctors must understand the capabilities and limitations of algorithms and make judgments accordingly. Integrating the doctor in the algorithm based decision making process is crucial for building trust in bigdata solutions and to foster understanding of (rare) diseases. One tool are interactive recommender systems (Holzinger et al. 2016) for medical applications. By allowing decision making with a doctor in the loop they utilize the knowledge and wisdom of doctors in conjunction with the rigor and conscientiousness of machine learning algorithms. The question is how to design interfaces and how to incorporate domain knowledge as to maximize support for the experts in this case: doctors. On the other side of the table we have patients. who want the best possible treatment and who want to understand their condition. The informed patient wants to know about their health status regularly and keep an eye on their health parameters. One problem is, that the need for information increases with age, because chronic diseases in particular require monitoring of health parameters, which increase with age. But age does not only increase the need for health surveillance, it also increases the diversity in the present population, regarding perceptual and cognitive capabilities. Therefore it is particularly necessary to address patient diversity when visualizing health data to a patient (Theis et al. 2016) Only human factors research and age dependent health visualization can help leverage the advantages of data driven health. Human Factors in Information Visualization and Decision Support Systems 4 4 Uncertainty and Trust Independent of their usage context, information visualization uses data from sources that are necessarily uncertain. While the data may be discrete and often correct, probabilistic algorithms and the open world assumption remind us that uncertainty is always present. The more pressing question is how does the visual representation of data incorporate the uncertainty and how can a user communicate their own uncertainty to a computer (Seipp et al. 2016) Scientific publications have an established modus operandi in reporting uncertainty. Disputable as they may be, publications based on quantitative data are seldom found without a p value or better confidence intervals, communicating the uncertainty in the data. Some of these measures are also found in information visualization or visual analytics, but research is still necessary when trying to visualize where uncertainty comes from (i.e. from the model, data, etc. Furthermore communicating uncertainty to the computer has hardly been investigated so far. This uncertainty can stem from differences in domain knowledge, experience with visualizations, or expertise in using a system at hand. Humanfactors research should address these differences and their implications on human computer interaction in both directions (Seipp et al. 2016