The amount of data available in the sports field is difficult for coaches, analysts, and players to comprehend using classic analytics methods. Thus, new methods are necessary to help users break down that information and analyze it at a deeper level. The BKViz visual analytics system focuses on individual basketball games using classic and novel methods to reveal how players perform together and as individuals. The information is presented in interactive visualizations that allow immediate user feedback.

Digital Humanities (DH) research and practice is subject to uncertainty during the life cycle of any project. Even in non dataoriented cases, analysts and other stakeholders need to make decisions without being aware of the level of uncertainty associated to the data being transformed by the computational tools used to enable the kind of novel work of humanists pursued within DH. We examine in this paper the literature that have characterized the types and sources of uncertainty in other fields, with the intent of establishing a foundation upon which build novel computational tools supporting the decision-making under uncertainty processes that DH is currently facing. We propose the use of progressive visual analytics as a feasible means to manage decision-making under uncertainty, which may help tackling some challenges related to the elimination or mitigation of uncertainty in DH, that otherwise would tamper the quality of the yielded results.

Vogt. 2016. A spatio-temporal visual analysis tool for historical dictionaries. In Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM '16), Francisco José García-Peñalvo (Ed.). ACM,

In soccer, understanding of collective tactical behavior has become an integral part in sports analysis at elite levels. Evolution of technology allows collection of increasingly larger and more specific data sets related to sport activities in cost-effective and accessible manner. All this information is minutely scrutinized by thousands of analysts around the globe in search of answers that can in the long-term help increase the performance of individuals or teams in their respective competitions. As the volume of data increases in size, so does the complexity of the problem and the need for suitable tools that leverage the cognitive load involved in the investigation. It is proven that visualization and computer-vision techniques, correctly applied to the context of a problem, help data analysts focus on the relevant information at each stage of the process, and generally lead to a better understanding of the facts that lie behind the data. In the current study, we presented a software prototype capable of assisting researchers and performance analysts in their duty of studying group collective behavior in soccer games and trainings. We used geospatial data acquired from a professional match to demonstrate its capabilities in two different case studies. Furthermore, we successfully proved the efficiency of the different visualization techniques implemented in the prototype and demonstrated how visual analysis can effectively improve some of the basic tasks employed by sports experts on their daily work, complementing more traditional approaches.

Historical dictionaries are colossal spatio-temporal artefacts comprising thousands of interrelated concepts and hosting a wide range of answers for cultural and/or historical inquiries. In our approach, the combination of thematic maps with network analysis and real-time textual querying of semantically-enriched lexicographical data serves as an entry point for the visual exploration of a large collection of records. Our tool aims to improve the comprehension of big data through visualization, thus helping the user to reach meaningful conclusions and acquire valuable insights into linguistic and other cultural issues in fast, easy ways.