Which operation in streaming data focuses on reducing occupied storage space?

The focus of the operation in streaming data that aims to reduce occupied storage space is compression. Compression techniques are utilized to decrease the amount of data needed to represent a given dataset without losing valuable information. This process helps optimize storage, making it more efficient by using less disk space or memory while still allowing for effective data retrieval and processing.

In the context of data management, compression can be particularly beneficial in streaming environments where large volumes of data are continuously generated. By compressing the data as it streams, organizations can minimize the impact on storage resources, which is crucial for maintaining performance and managing costs.

While other operations like dimensionality reduction and summarization contribute to data processing and storage efficiency, they do so in different ways. Dimensionality reduction simplifies datasets by removing less significant features, which may not always lead to a direct reduction in storage needs. Summarization provides a concise representation of the data but does not inherently reduce the amount of data stored. Visualization, on the other hand, is primarily concerned with representing data graphically for analysis and decision-making, rather than with storage efficiency.