Information and Software Visualization (ISOVIS)

The Linnaeus University Centre for Data Intensive Sciences and Applications (DISA) focuses its efforts on open questions in collection, analysis and utilization of large data sets. With its core in computer science, it takes a multidisciplinary approach and collaborates with researchers from all faculties at the university.

Andreas Kerren, chair of ISOVIS, was co-applicant of DISA and is member of the DISA coordination board. Visualization and visual analytics are key technologies for (big) data analysis and therefore naturally connected to DISA and its main goals. The broad expertise of the ISOVIS members within several subfields of information visualization and visual analytics reflects the diversity of data sets and analysis problems that analysts/researchers from all faculties of the university are facing in their daily work. ISOVIS can provide answers to many of such analytical challenges.

As applied machine learning approaches are more and more common in DISA, we also contribute by making the black boxes of the analysis methods and resulting models transparent for the analyst. For this, and for providing reliable trust into the models, we develop foundational visual analytics principles, techniques and tools for analyzing both data and machine learning models.

Smarter Systems is a complete knowledge environment focusing on the systems and challenges of the future. It addresses challenges together with its partners through education and research initiatives in collaboration. Developers of complex systems need to be able to understand the system structure and behavior at any point in time, even when the system has evolved based on the learned knowledge.

Here, visual analytics is crucial for improving the understanding of the complex, dynamic and evolving data and processes that occur at both levels: when engineering smarter systems as well as during their operation. In consequence, we as humans are then able to understand their structure and behavior anytime, even when they have evolved based on the learned knowledge.

In recent years, as a result of developments in sensor and communication technologies, the amount of data collected by complex systems has increased exponentially. Engineers, operators and other stakeholders use these data sets to monitor the processes that systems are situated in or within the system itself. However, standard tools that visualize (or analyze) the data do not provide the required capabilities and analytical features: new means to make effective use of this data are needed.

There are three grand challenges in this context that are addressed by ISOVIS:

1. Dealing with the four Vs of big data analysis (volume, variety, veracity, velocity) by using visual analysis
   

   Smarter systems set specific challenges to the analysis of data, such as uncertainty in the data or the sheer size and complexity of real-world data. Those issues in combination with temporal (streaming) aspects put high demands on a visual analysis (VA) solution.
   Tackling the first grand challenge will contribute to achieving key objective 2 for the knowledge environment Smarter Systems: To devise engineering processes for perpetual adaptation and evolution. In particular, advanced VA techniques will contribute to the design and application of engineering processes for perpetual adaptation and evolution.

2. Transfer of analytics methods, processes and tools to smarter systems

   Most current VA tools were solely developed and aligned to analytics tasks for specific applications. The reuse of VA components, and the transfer from other application domains to Smarter Systems, is highly desirable but particularly challenging. Tackling our second grand challenge will contribute to achieving key objective 2 by transferring analytics methods, processes and tools of established domains to Smarter Systems, supporting the engineering processes.
3. Explainability of machine learning models by using visual analytics
   Opening the black box of computational models, such as supervised or unsupervised machine learning, is a recent challenge in VA. This area is also called explainable AI in general, and visualization tools for machine learning are able to increase the trust into such methods. Tackling the third grand challenge will contribute to both assuring product capabilities in uncertain conditions (key objective 1) and devising smart engineering processes (key objective 2). This is done through the development of proper methods and techniques to better understand the systems and to support their operators in concrete real-world settings.

ISOVIS is responsible for the following courses:

Bachelor courses

• 1DV437, Introduction to Game Programming, 7.5 credits 
• 1DV512, Operating Systems, 7.5 credits 
• 1DV513/2DV513, Database Theory, 7.5 credits  
• 1DV800, Computer Graphics, 7.5 credits
• 1ME326, Web Technology 6, 7.5 credits 
• 2DV505, Current Topics within Computer Science, 7.5 credits

Master courses

• 2ME301, Scientific Methods in Media Technology, 7.5 credits 
• 4DV504, Selected Topics in Computer Science, 5 credits 
• 4DV507, Code Transformation and Interpretation, 5 credits 
• 4DV510, Data Mining, 5 credits 
• 4DV805, Information Visualization, 5 credits
• 4DV806, Advanced Information Visualization and Applications, 5 credits 
• 4DV807, Project in Visualization and Data Analysis, 10 credits 
• 4DV808, Computational and Visual Text Analysis, 5 credits
• 4DV809, Computational and Visual Network Analysis, 5 credits 
• 4ME302, Foundations of Computational Media, 7.5 credits 
• 4ME305, Web and Mobile Development, 7.5 credits