Answer :
Final Answer:
d. data time-horizon difference, data update difference, and data redundancy difference.The correct option is D.
Explanation:
Normalization of data involves organizing and structuring data in a consistent and efficient manner to eliminate redundancy, inconsistencies, and anomalies. This is typically necessary when there are differences in data attributes like update frequency, redundancy, and time-horizon, as mentioned in options a, b, c, and e. However, when all three factors mentioned in option d (data time-horizon difference, data update difference, and data redundancy difference) are present, it becomes unnecessary to normalize the data.
In this scenario, the differences in data attributes are extensive enough that normalization might not provide significant benefits. This situation can arise when dealing with highly diverse and heterogeneous data sources with varying levels of update frequency, redundancy, and temporal characteristics. In such cases, the effort required to normalize the data might outweigh the advantages gained from doing so.
Normalization of data involves transforming data into a standardized format to improve data integrity and consistency. It helps in reducing data anomalies and redundancies, making it easier to manage and analyze. However, normalization might not always be suitable for extremely diverse datasets where the mentioned differences are prominent. In such cases, other data management strategies, such as data federation or virtualization, could be considered to handle the complexities posed by the varying attributes of the data sources.There is the option D is correct.
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