TY - GEN A1 - Koch, Christian A1 - Riehle, Dirk A1 - Müller, Katharina T1 - Schema Inference for Tabular Data with Explainable Artificial Intelligence BT - Systematic Review Protocol - Third Version N2 - 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. KW - Explainable Artificial Intelligence KW - Data Engineering KW - Data Science KW - Explainable Artificial Intelligence KW - Government KW - Schema Inference Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-15601 SP - 2 EP - 6 ER - TY - GEN A1 - Koch, Christian A1 - Riehle, Dirk T1 - Schema Inference with Explainable AI for Data Engineering in Government Institutions BT - Systematic Review Protocol - Second Version N2 - 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 with explainable AI (XAI) for data engineering in government institutions. This second version includes adjustments resulting from the first iteration of the review. KW - Explainable Artificial Intelligence KW - Data Engineering KW - Data Science KW - Explainable Artificial Intelligence KW - Government KW - Schema Inference Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-15059 SP - 2 EP - 6 ER - TY - GEN A1 - Koch, Christian A1 - Riehle, Dirk T1 - Schema Inference with Explainable AI for Data Engineering in Government Institutions BT - Systematic Review Protocol N2 - 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 with explainable AI (XAI) for data engineering in government institutions. KW - Explainable Artificial Intelligence KW - Explainable Artificial Intelligence KW - Schema Inference KW - Data Engineering KW - Data Science KW - Government Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-11609 SP - 2 EP - 6 ER -