Streaming algorithms

Streaming algorithms, also known as online algorithms or data stream algorithms, are algorithms designed to process large volumes of data that arrive in a continuous flow, or stream, rather than in a fixed-size batch. Because data streams can be enormous and potentially unbounded, streaming algorithms prioritize efficiency in terms of time and space, making them suitable for real-time applications.

The Boyer–Moore majority vote algorithm is an efficient algorithm used to identify the majority element in a list or array. An element is considered a majority if it appears more than half the times (i.e., \( \frac{n}{2} \) times, where \( n \) is the total number of elements) in the array.

The Lossy Counting Algorithm is a streaming algorithm designed for the estimation of frequency counts of items in a data stream. It's particularly useful when dealing with large volumes of data where it is impractical to store and count each individual element due to memory constraints. The primary goal of the Lossy Counting Algorithm is to maintain an approximate count of elements that may exceed a certain frequency threshold.

The Misra-Gries algorithm is a classic algorithm in computer science that is used to identify "heavy hitters" in a data stream. A heavy hitter is defined as an element whose frequency of occurrence in the stream exceeds a certain threshold. This kind of problem is particularly relevant in scenarios like network traffic monitoring, data mining, and streaming data analysis.

The Misra–Gries algorithm is a streaming algorithm used for identifying the most frequent elements in a data stream. It was developed by Sudhakar Misra and Raghunathan Gries in 1982. This algorithm allows us to track and summarize large sequences of data efficiently, using a limited amount of memory, making it particularly suited for situations where the entire data set cannot fit into memory.

The One-pass algorithm, also known as a streaming algorithm or online algorithm, refers to a class of algorithms designed to process a data stream in a single pass, meaning that they can analyze or summarize data without needing to store the entire dataset in memory at once. This makes one-pass algorithms particularly useful for handling large datasets that exceed memory capacity.

A streaming algorithm is a type of algorithm designed to process data that arrives in a continuous flow, often referred to as "data streams." These algorithms are particularly useful for managing large volumes of data that cannot be stored completely in memory (due to size constraints) or when processing time is critical. ### Key Characteristics of Streaming Algorithms: 1. **Limited Memory Usage**: Streaming algorithms typically utilize a small, fixed amount of memory regardless of the size of the dataset.