Abstract
A state-of-the-art Data Fabric architecture and Data Mesh solution is unquestionably linked to the knowledge catalog as one of its prime components. What differentiates a modern knowledge catalog from traditional ones are AI-infused capabilities to automate tasks and to provide self-service capabilities. AI is without dispute an inevitable domain that characterizes a modern Data Fabric and Data Mesh. Infusing AI generates additional added value specifically for business users, such as delivering trustworthy AI.
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Notes
- 1.
See References [1] and [2] for more information on knowledge catalogs and metadata management solutions available by different vendors.
- 2.
See Reference [3] for Gartner’s market guide for active metadata management.
- 3.
See Reference [4] for more information on data curation.
- 4.
See Reference [5] for more information on semantic knowledge graphs in the context of a Data Fabric.
- 5.
See Reference [6] for more information on Amazon Neptune.
- 6.
See Reference [7] for more information on Neo4j.
- 7.
See Reference [8] for more information on IBM Graph.
- 8.
We further examine these and other quality metrics, for example, the F1 measure, in the section on trustworthy AI later in this chapter.
- 9.
See References [9] and [10] for details on guidelines of the European Commission and the US Department of State regarding trustworthy AI.
- 10.
See Reference [11] for more information on trustworthy AI, including social aspects.
- 11.
We are using the terms fairness and bias interchangeably. However, fairness has a more social connotation, whereas bias is used more in a mathematical context.
- 12.
See References [12] for more details on IBM Watson OpenScale, which this example is based on.
- 13.
We elaborate on additional AI model quality metrics further in this chapter in the following.
- 14.
Refer to Reference [13] for a comparison of the LIME and SHAP methods.
- 15.
See References [14] and [15] for good introductions of ML, where the concepts of the confusion matrix, areas under the ROC and PR curves, etc. are explained.
- 16.
See Chapter 7 for more information on activating the digital exhaust.
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Hechler, E., Weihrauch, M., Wu, Y.(. (2023). Key Data Fabric and Data Mesh Capabilities. In: Data Fabric and Data Mesh Approaches with AI. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-9253-2_5
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DOI: https://doi.org/10.1007/978-1-4842-9253-2_5
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