Details
| Original language | English |
|---|---|
| Article number | 102299 |
| Number of pages | 16 |
| Journal | Data and Knowledge Engineering |
| Volume | 151 |
| Early online date | 12 Mar 2024 |
| Publication status | Published - May 2024 |
Abstract
Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.
Keywords
- Anomaly explanation, Commonality plots, Data visualization, Decision support, Subset-dataset relationships, Visual analytics
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
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In: Data and Knowledge Engineering, Vol. 151, 102299, 05.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Insights into commonalities of a sample
T2 - A visualization framework to explore unusual subset-dataset relationships
AU - Stege, Nikolas
AU - Breitner, Michael H.
PY - 2024/5
Y1 - 2024/5
N2 - Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.
AB - Domain experts are driven by business needs, while data analysts develop and use various algorithms, methods, and tools, but often without domain knowledge. A major challenge for companies and organizations is to integrate data analytics in business processes and workflows. We deduce an interactive process and visualization framework to enable value creating collaboration in inter- and cross-disciplinary teams. Domain experts and data analysts are both empowered to analyze and discuss results and come to well-founded insights and implications. Inspired by a typical auditing problem, we develop and apply a visualization framework to single out unusual data in general subsets for potential further investigation. Our framework is applicable to both unusual data detected manually by domain experts or by algorithms applied by data analysts. Application examples show typical interaction, collaboration, visualization, and decision support.
KW - Anomaly explanation
KW - Commonality plots
KW - Data visualization
KW - Decision support
KW - Subset-dataset relationships
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85188808893&partnerID=8YFLogxK
U2 - 10.1016/j.datak.2024.102299
DO - 10.1016/j.datak.2024.102299
M3 - Article
AN - SCOPUS:85188808893
VL - 151
JO - Data and Knowledge Engineering
JF - Data and Knowledge Engineering
SN - 0169-023X
M1 - 102299
ER -