Integrating Explicit Knowledge in the Visual Analytics Process - Knowledge-assisted Visual Analytics Methods for Time-oriented Data

Markus Wagner

Abstract

In this paper, I describe my thesis project for the integration of explicit knowledge from domain experts into the visual analytics process. As base for the implementation of the research project, I will follow the nested model for visualization design and validation. Additionally, I use a problem-driven approach to study knowledge-assisted visualization systems for time-oriented data in the context of real world problems. At first, my research will focus on the ITsecurity domain where I analyze the needs of malware analysts to support them during their work. Therefore I have currently prepared a problem characterization and abstraction to understand the needs of the domain experts to gain more insight into their workflow. Based on that findings, I am currently working on the design and the implementation of a prototype. Next, I will evaluate these visual analytics methods and finally I will test the generalizability of the knowledgeassisted visual analytics methods in a second domain.

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Paper Citation


in Harvard Style

Wagner M. (2015). Integrating Explicit Knowledge in the Visual Analytics Process - Knowledge-assisted Visual Analytics Methods for Time-oriented Data . In Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015) ISBN , pages 9-18


in Bibtex Style

@conference{dcvisigrapp15,
author={Markus Wagner},
title={Integrating Explicit Knowledge in the Visual Analytics Process - Knowledge-assisted Visual Analytics Methods for Time-oriented Data},
booktitle={Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015)},
year={2015},
pages={9-18},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015)
TI - Integrating Explicit Knowledge in the Visual Analytics Process - Knowledge-assisted Visual Analytics Methods for Time-oriented Data
SN -
AU - Wagner M.
PY - 2015
SP - 9
EP - 18
DO -