Visual Analytics for Rule-Based Quality Management of Multivariate Data

Florian Spechtenhauser
Visual Analytics for Rule-Based Quality Management of Multivariate Data
[thesis]

Information

Abstract

Ensuring an appropriate data quality is a critical topic when analyzing the ever increasing amounts of data collected and generated in today’s world. Depending on the given task, even sophisticated analysis methods may cause misleading results due to an insufficient quality of the data set at hand. In this case, automated plausibility checks based on defined rules are frequently used to detect data problems such as missing data or anomalies. However, defining such rules and using their results for an efficient data quality assessment is a challenging topic. Visualization is powerful to reveal unexpected problems in the data, and can additionally be used to validate results of applied automated plausibility checks. Visual Analytics closes the gap between automated data analysis and visualization by providing means to guide the definition and optimization of plausibility checks in order to use them for a continuous detection and validation of problems detected in the data. This diploma thesis provides a design study of a Visual Analytics approach, called Data Quality Overview, which provides a detailed, yet scalable summary of the results of defined plausibility checks, and includes means for validation and investigation of these results at various levels of detail. The approach is based on a detailed task analysis of data quality assessment, and is validated using a case study based on sensor data from the energy sector in addition to feedback collected from domain experts.

Additional Files and Images

Additional images and videos

Additional files

Weblinks

No further information available.

BibTeX

@mastersthesis{Spechtenhauser_Florian_2016,
  title =      "Visual Analytics for Rule-Based Quality Management of
               Multivariate Data",
  author =     "Florian Spechtenhauser",
  year =       "2016",
  abstract =   "Ensuring an appropriate data quality is a critical topic
               when analyzing the ever increasing amounts of data collected
               and generated in today’s world. Depending on the given
               task, even sophisticated analysis methods may cause
               misleading results due to an insufficient quality of the
               data set at hand. In this case, automated plausibility
               checks based on defined rules are frequently used to detect
               data problems such as missing data or anomalies. However,
               defining such rules and using their results for an efficient
               data quality assessment is a challenging topic.
               Visualization is powerful to reveal unexpected problems in
               the data, and can additionally be used to validate results
               of applied automated plausibility checks. Visual Analytics
               closes the gap between automated data analysis and
               visualization by providing means to guide the definition and
               optimization of plausibility checks in order to use them for
               a continuous  detection and validation of problems detected
               in the data. This diploma thesis provides a design study of
               a Visual Analytics approach, called Data Quality Overview,
               which provides a detailed, yet scalable summary of the
               results of defined plausibility checks, and includes means
               for validation and investigation of these results at various
               levels of detail. The approach is based on a detailed task
               analysis of data quality assessment, and is validated using
               a case study based on sensor data from the energy sector in
               addition to feedback collected from domain experts.",
  month =      aug,
  address =    "Favoritenstrasse 9-11/186, A-1040 Vienna, Austria",
  school =     "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2016/Spechtenhauser_Florian_2016/",
}