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Task 3.1 deals with repairing and cleaning data sets according to their quality. While repairing is about purely logical constraints, cleaning intents to fix problem related to data itself. One of the possible examples for cleaning task is to prove whether literal type correspond to the type defined by the schema. The first step towards cleanup concerns with detection of quality problems. Since estimation of quality will be done in task 5.2, only detailed quality problem description is missing and should be implemented
The text was updated successfully, but these errors were encountered:
The current implementation of metrics has a method which can output quality problems. Feel free to open this issue if the current implementation is not satisfactory.
Task 3.1 deals with repairing and cleaning data sets according to their quality. While repairing is about purely logical constraints, cleaning intents to fix problem related to data itself. One of the possible examples for cleaning task is to prove whether literal type correspond to the type defined by the schema. The first step towards cleanup concerns with detection of quality problems. Since estimation of quality will be done in task 5.2, only detailed quality problem description is missing and should be implemented
The text was updated successfully, but these errors were encountered: