Tobias Bleifuß
Dmitri V. Kalashnikov
Felix Naumann
Divesh Srivastava
ABSTRACT
Data and metadata suffer many different kinds of change: values are inserted, deleted or updated; entities appear and disappear; properties are added or re-purposed, etc. Explicitly recognizing, exploring, and evaluating such change can alert to changes in data ingestion procedures, can help assess data quality, and can improve the general understanding of the dataset and its behavior over time. We propose a data model-independent framework to formalize such change. Our change-cube enables exploration and discovery of such changes to reveal dataset behavior over time.
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