@conference {947,
	title = {Chatbot Explorer: Towards an understanding of knowledge bases of chatbot systems},
	booktitle = {30th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2022},
	year = {2022},
	abstract = {A chatbot can automatically process a user{\textquoteright}s request, e.g. to provide a requested information. In doing so, the user starts a conversation with the chatbot and can specify the request by further inquiry. Due to the developments in the field of NLP in recent years, algorithmic text comprehension has been significantly improved. As a result, chatbots are increasingly used by companies and other institutions for various tasks such as order processes or service requests. Knowledge bases are often used to answer users queries, but these are usually curated manually in various text files, prone to errors. Visual methods can help the expert to identify common problems in the knowledge base and can provide an overview of the chatbot system. In this paper, we present Chatbot Explorer, a system to visually assist the expert to understand, explore, and manage a knowledge base of different chatbot systems. For this purpose, we provide a tree-based visualization of the knowledge base as an overview. For a detailed analysis, the expert can use appropriate visualizations to drill down the analysis to the level of individual elements of a specific story to identify problems within the knowledge base. We support the expert with automatic detection of possible problems, which can be visually highlighted. Additionally, the expert can also change the order of the queries to optimize the conversation lengths and it is possible to add new content. To develop our solution, we have conducted an iterative design process with domain experts and performed two user evaluations. The evaluations and the feedback from our domain experts have shown that our solution can significantly improve the maintainability of chatbot knowledge bases.},
	author = {Alrik Hausdorf and Lydia M{\"u}ller and Gerik Scheuermann and Andreas Niekler and Daniel Wiegreffe}
}
@proceedings {840,
	title = {LocalCompanies: Visual Analytics of spatial aligned regional companies},
	year = {2020},
	address = {Leipzig},
	doi = {10.31219/osf.io/tsdfh},
	url = {osf.io/tsdfh},
	author = {Alrik Hausdorf and Andreas Niekler and Daniel Wiegreffe}
}
@article {879,
	title = {Masakari: visualization supported statistical analysis of genome segmentations},
	journal = {BMC Bioinformatics},
	volume = {21},
	year = {2020},
	month = {10/2020},
	chapter = {437},
	abstract = {Background

In epigenetics, the change of the combination of histone modifications at the same genomic location during cell differentiation is of great interest for understanding the function of these modifications and their combinations. Besides analyzing them locally for individual genomic locations or globally using correlations between different cells types, intermediate level analyses of these changes are of interest. More specifically, the different distributions of these combinations for different cell types, respectively, are compared to gain new insights.

Results and discussion

We propose a new tool called {\textquoteleft}Masakari{\textquoteright} that allows segmenting genomes based on lists of ranges having a certain property, e.g., peaks describing histone modifications. It provides a graphical user interface allowing to select all data sets and setting all parameters needed for the segmentation process. Moreover, the graphical user interface provides statistical graphics allowing to assess the quality and suitability of the segmentation and the selected data.

Conclusion

Masakari provides statistics based visualizations and thus fosters insights into the combination of histone modification marks on genome ranges, and the differences of the distribution of these combinations between different cell types.},
	doi = {10.1186/s12859-020-03761-6},
	url = {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03761-6},
	author = {Dirk Zeckzer and Alrik Hausdorf and Nicole Hinzmann and Lydia M{\"u}ller and Daniel Wiegreffe}
}
@proceedings {807,
	title = {SyCaT-Vis: Visualization-Based Support of Analyzing System Behavior based on System Call Traces},
	year = {2019},
	address = {Pilsen, Czech Republic},
	abstract = {Detecting anomalies in the behavior of a computer system is crucial for determining its security. One way of detecting these anomalies is based on the assessment of the amount and sequence of system calls issued by processes. While the number of processes on a computer can become very large, the number of system calls issued during the lifespan of such a process and its subprocesses can be humongous. In order to decide whether these anomalies are due to the intended system usage or if they are caused by malicious actions, this humongous amount of data needs being analyzed. Thus, a careful analysis of the system calls{\^a}{\texteuro}{\texttrademark} types, their amount, and their temporal sequence requires sophisticated support. Visualization is frequently used for this type of tasks. Starting with a carefully aggregation of the data presented in an overview representation, the quest for information is supported by carefully crafted interactions. These allow filtering the tremendous amount of data, thus removing the standard behavior data and leaving the potentially suspicious one. The latter can then be investigated on increasingly finer levels. Supporting this goal-oriented analysis, we propose novel interactive visualizations implemented in the tool SyCaT-Vis. SyCaT-Vis fosters obtaining important insights into the behavior of computer systems, the processes executed, and the system call sequences issued.},
	keywords = {behavior analysis, security analysis, Security visualization, system call traces},
	doi = {https://doi.org/10.24132/CSRN.2019.2901.1.6},
	url = {http://wscg.zcu.cz/wscg2019/2019-papers/!!_CSRN-2801-6.pdf},
	author = {Alrik Hausdorf and Nicole Hinzmann and Dirk Zeckzer}
}
@proceedings {719,
	title = {iDotter - an interactive dot plot viewer},
	year = {2017},
	month = {08/2017},
	pages = {117-124},
	address = {Pilsen, Czech Republic},
	abstract = {Bioinformaticians judge the likelihood of the overall RNA secondary structure based on comparing its base pair probabilities. These probabilities can be calculated by various tools and are frequently displayed using dot plots for further analysis. However, most tools produce only static dot plot images which restricts possible interactions to the capabilities of the respective viewers (mostly PostScript-viewers). Moreover, this approach does not scale well with larger RNAs since most PostScript viewers are not designed to show a huge number of elements and have only legacy support for PostScript. Therefore, we developed iDotter, an interactive tool for analyzing RNA secondary structures. iDotter overcomes the previously described limitations providing multiple interaction mech- anisms facilitating the interactive analysis of the displayed data. According to the biologists and bioinformaticians that regularly use out interactive dot plot viewer, iDotter is superior to all previous approaches with respect to facilitating dot plot based analysis of RNA secondary structures.},
	keywords = {Bioinformatics Visualization, Dot Plots, Tabular Data, User Interfaces},
	isbn = {978-80-86943-49-7},
	author = {Daniel Wiegreffe and Alrik Hausdorf and Sebastian Z{\"a}nker and Dirk Zeckzer}
}
