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@Select Types{,
Journal = "Band-1",
Title= "Towards a Taxonomy of Data Heterogeneity",
Author= "Jan Roeder, Jan Muntermann, and Thomas Kneib",
Doi= "https://doi.org/10.30844/wi_2020_c6-roeder",
Abstract= "The increasing diversity of data available today poses a multitude of challenges to researchers and practitioners. Data understanding, i.e., describing, exploring, and verifying a data set at hand, becomes a critical process during which it is examined if data complies with the actual user needs. With an increasing complexity of the data universe accessible by organizations and decision-makers, this task has become even more important and challenging. Building on insights from information systems research, computer science, and statistics, we develop and evaluate a taxonomy of data heterogeneity for addressing this challenge. The proposed taxonomy provides a foundation for exploring the properties of data sets. Thereby, it is relevant for both researchers and practitioners as it provides a useful tool for describing and ultimately understanding data sets. We illustrate the effectiveness of our taxonomy by applying it to data sets available to the research community and industry.
",
Keywords= "data science, data heterogeneity, data understanding, taxonomy, information value chain
",
}
Jan Roeder, Jan Muntermann, and Thomas Kneib: Towards a Taxonomy of Data Heterogeneity. Online: https://doi.org/10.30844/wi_2020_c6-roeder (Abgerufen 23.11.24)
Open Access
Abstract
Abstract
The increasing diversity of data available today poses a multitude of challenges to researchers and practitioners. Data understanding, i.e., describing, exploring, and verifying a data set at hand, becomes a critical process during which it is examined if data complies with the actual user needs. With an increasing complexity of the data universe accessible by organizations and decision-makers, this task has become even more important and challenging. Building on insights from information systems research, computer science, and statistics, we develop and evaluate a taxonomy of data heterogeneity for addressing this challenge. The proposed taxonomy provides a foundation for exploring the properties of data sets. Thereby, it is relevant for both researchers and practitioners as it provides a useful tool for describing and ultimately understanding data sets. We illustrate the effectiveness of our taxonomy by applying it to data sets available to the research community and industry.
Keywords
Schlüsselwörter
data science, data heterogeneity, data understanding, taxonomy, information value chain
References
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