Streaming algorithms are specialized techniques designed to process and analyze large data streams in a single pass while using limited memory resources. These algorithms provide approximate answers to various problems, making them crucial for handling real-time data efficiently, especially in contexts where storage and computational resources are constrained. They are particularly valuable when dealing with randomized approximation algorithms and submodular functions, as they can quickly provide insights from extensive datasets without needing to store all the data.
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