Future Work

The presented contributions of this thesis serve as a baseline for several extensions to be addressed as future work. Future research directions on the individual visual analytics systems presented as technical contributions of this thesis are discussed in the respective chapters (Chapter4–9)

Applying the design methodology to additional scenarios:In this thesis, we already successfully applied our design methodology on six decision making-related scenarios. These served as a proof of concept. In the future, we plan to apply the methodology to additional scenarios. In addition, we pro-mote other researchers to apply our methodology. This has two positive effects. First, the methodology can be further tested towards its suitability. Second, if shortcomings are identified it can be improved.

In our approach, we presented three visual analytics systems targeting textual data. Text as unstructured data category is difficult to handle. Text analysis methods that support humans in extracting knowledge from text are difficult to comprehend. Therefore, we see great potential for combining text analysis with visualization techniques to support decision making. Methods like text summarization, topic modeling, text classification, etc. provide great potential for informing the decision making process. The same holds for empirical datasets. Most of the related work on visual analytics addresses this data category, but only few explicitly target decision support. Furthermore, the combination of modeling approaches with visualization bears great potential for future research. For example, recently, tremendous research on deep neural networks has been conducted. Resulting approaches are also applied to the business context. However, the underlying methods are difficult to comprehend. We promote further research in combining visualization and neural networks to reduce the complexity of the algorithms and make them applicable in decision making scenarios.

Extending the design methodology to other types of unstructured data: The focus of this thesis has been laid on the analysis of textual, empirical, and model-driven data. However, additional data categories exist. For example, unstructured audio, video, and image data play an increasingly important

10.2. Future Work

role in decision making. Therefore, in the future it would be interesting to apply our methodology to these data categories. As an example existing algorithms allow the extraction of text from speech in video and audio data. This text can be further processed as described in this thesis.

Extending the concept for bridging gaps to other domains:In this thesis, we introduced a concept for bridging knowledge gaps between stakeholders involved in decision making. Collaborative visual analytics has already been targeted by related research approaches, e.g. by Isenberg et al. [IES^∗11].

However, besides time (asynchronous vs. synchronous) and space (co-located vs. distributed) the level of expertise (expert vs. non-expert) should be added to their model in order to support the communi-cation, e.g., between scientists and decision makers. In this thesis, we presented a concept specifically targeting decision making. However, collaboration is critical for several domains. Therefore, we pro-mote to further generalize the presented concept, or to define additional knowledge bridging concepts adapted to other domains.

Defining analysis workflow patterns for complexity reduction and result presentation: In this thesis, we realized the bridging of knowledge gaps via interactive visualization. We focused on basic visualization techniques in order to allow stakeholders with different expertise levels to communicate on the same information basis. In most cases, this implies a trade-off between analysis functionality and usability of the visualization. For example, in Chapter5, we presented a visual-interactive text analysis workflow system that integrates multiple analysis features but can mainly be used by analysts with a background in data analysis. To mitigate this trade-off, we propose the design of workflow patterns that can be used to encode best practice workflows including parameterization, filter settings, etc. (e.g., as presented by the DimStiller approach [IMI^∗10]). These best practices can be recommended to novice users to simplify their access to the visual analytics systems. In addition, analytics process capturing methods can support analysts in the presentation of results to decision makers. Research in visual analytics story-telling can contribute to the dissemination of information and improve the transparency of the analysis process [KM13].

Integrating individual visual analytics components to single framework: In our approach, we presented different visual analytics systems that target different datasets and tasks. As a future task, these components could be integrated into a single system. This would allow using analysis results from one component as input for another. We have already published an integrated version that combines the simulation, the optimization, and an additional sentiment analysis component to a visual-interactive system [RBMK14]. However, we promote further research in integrating different datasets and tasks into a single system.

Realizing device-adaptive interfaces.Mobility plays in important role for decision makers. Access to information and analysis functionalities needs to be granted from different locations and devices.

This poses challenges to visual analytics, since solutions need to be provided as web applications and on mobiles with small screens. From the technical perspective, the easiest solution is the realization of responsive design as commonly realized for web applications. However, since visualization design targets the exploitation of the available screen space, this is not always a trivial task. An alternative solution would be the reduction of the presented information. The analysis results can be aggregated to the most relevant information and provide simple infographics as output of the visual analytics process.

Acceleration of evaluation processes. Finally, as already discussed, decision making processes are time-critical. At the same time, the user-centered design process that ensures the usability and usefulness of visual analytics solutions is time-consuming. In order to provide useful and usable visual analytics solutions for decision making scenarios, evaluation processes need to be accelerated. As an option, current usability heuristics may be adapted to the decision making domain. Moreover, user interaction logging may support evaluation. Additionally, we recommend the evaluation of generic visualization techniques with different user groups, including non-expert users, in order to provide guidelines for the design of easy-to-use visual analytics systems.