Schema Inference for Tabular Data with Explainable Artificial Intelligence
- Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.
Author: | Christian KochORCiD, Dirk RiehleORCiD, Katharina MüllerORCiD |
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URN: | urn:nbn:de:bvb:92-opus4-15601 |
DOI: | https://doi.org/10.34646/thn/ohmdok-1560 |
Subtitle (English): | Systematic Review Protocol - Third Version |
Document Type: | Other |
Language: | English |
Date of first Publication: | 2024/05/08 |
Publishing Institution: | Technische Hochschule Nürnberg Georg Simon Ohm |
Release Date: | 2024/05/10 |
Tag: | Data Engineering; Data Science; Explainable Artificial Intelligence; Government; Schema Inference |
GND Keyword: | Explainable Artificial Intelligence |
Pagenumber: | 7 |
First Page: | 2 |
Last Page: | 6 |
institutes: | Fakultät Betriebswirtschaft |
Dewey Decimal Classification: | 0 Informatik, Informationswissenschaft, allgemeine Werke |
Research Themes: | Digitalisierung & Künstliche Intelligenz |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |