evaline komu
more than 2 weeks ago
Types of data anomalies.
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ChatGPT
more than 2 weeks ago
1. Insertion anomaly: This occurs when new data cannot be added to the database without also adding unrelated data.
2. Deletion anomaly: This occurs when deleting data from the database unintentionally removes other related data.
3. Update anomaly: This occurs when updating data in the database results in inconsistencies or errors in other related data.
4. Redundancy anomaly: This occurs when the same data is stored in multiple locations in the database, leading to inconsistencies and potential errors.
5. Inconsistency anomaly: This occurs when data in the database is inconsistent or contradictory, leading to inaccuracies and confusion.
6. Dependency anomaly: This occurs when data in the database is dependent on other data that may change or be deleted, leading to errors and inconsistencies.
2. Deletion anomaly: This occurs when deleting data from the database unintentionally removes other related data.
3. Update anomaly: This occurs when updating data in the database results in inconsistencies or errors in other related data.
4. Redundancy anomaly: This occurs when the same data is stored in multiple locations in the database, leading to inconsistencies and potential errors.
5. Inconsistency anomaly: This occurs when data in the database is inconsistent or contradictory, leading to inaccuracies and confusion.
6. Dependency anomaly: This occurs when data in the database is dependent on other data that may change or be deleted, leading to errors and inconsistencies.
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Google Gemini AI
more than 2 weeks ago
Data Quality Anomalies
- Missing values: Data is missing for some records.
- Invalid values: Data is present but does not conform to the expected format or range.
- Outliers: Data values that are significantly different from the majority of the data.
- Duplicates: Multiple records have identical data values.
- Inconsistent values: Data values that contradict each other within a record or across records.
- Typographical errors: Data contains spelling, grammar, or formatting errors.
Structural Anomalies
- Missing fields: Data does not contain all the necessary fields for a complete record.
- Extra fields: Data contains fields that are not defined as part of the schema.
- Incorrect field types: Data is stored in a field with an incompatible data type.
- Field length inconsistencies: Data values in a field vary in length or format.
- Data skew: Data is not evenly distributed across all fields or records.
Semantic Anomalies
- Ambiguous data: Data has multiple interpretations or meanings.
- Inaccurate data: Data does not accurately represent the real-world entity it represents.
- Inconsistent definitions: Data is defined differently in different systems or processes.
- Lack of referential integrity: Data in one table is not consistent with data in another related table.
- Redundant data: Data is stored multiple times in different tables or fields.
Temporal Anomalies
- Data staleness: Data is outdated and does not reflect the current state of the world.
- Temporal inconsistencies: Data values have different timestamps or dates, indicating an incorrect temporal order.
- Untimely data: Data is received or processed too late to be useful.
- Missing values: Data is missing for some records.
- Invalid values: Data is present but does not conform to the expected format or range.
- Outliers: Data values that are significantly different from the majority of the data.
- Duplicates: Multiple records have identical data values.
- Inconsistent values: Data values that contradict each other within a record or across records.
- Typographical errors: Data contains spelling, grammar, or formatting errors.
Structural Anomalies
- Missing fields: Data does not contain all the necessary fields for a complete record.
- Extra fields: Data contains fields that are not defined as part of the schema.
- Incorrect field types: Data is stored in a field with an incompatible data type.
- Field length inconsistencies: Data values in a field vary in length or format.
- Data skew: Data is not evenly distributed across all fields or records.
Semantic Anomalies
- Ambiguous data: Data has multiple interpretations or meanings.
- Inaccurate data: Data does not accurately represent the real-world entity it represents.
- Inconsistent definitions: Data is defined differently in different systems or processes.
- Lack of referential integrity: Data in one table is not consistent with data in another related table.
- Redundant data: Data is stored multiple times in different tables or fields.
Temporal Anomalies
- Data staleness: Data is outdated and does not reflect the current state of the world.
- Temporal inconsistencies: Data values have different timestamps or dates, indicating an incorrect temporal order.
- Untimely data: Data is received or processed too late to be useful.
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