Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON

MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.

c++ LINQ -like library of higher-order functions for data manipulation

Dynatrace hash library for Java

Performant implementations of various streaming algorithms, including Count–min sketch, Top k, HyperLogLog, Reservoir sampling.

Estimating k-mer coverage histogram of genomics data

Learning M-Way Tree - Web Scale Clustering - EM-tree, K-tree, k-means, TSVQ, repeated k-means, bitwise clustering

t-digest module for Redis

Federated Principal Component Analysis Revisited!

Streaming operations on NumPy arrays

A Set of Streaming Algorithms in C++, Python, and Go

An online statistics library, written in Go

Streaming, Memory-Limited, r-truncated SVD Revisited!

RiverText is a framework that standardizes the Incremental Word Embeddings proposed in the state-of-art. Please feel welcome to open an issue in case you have any questions or a pull request if you want to contribute to the project!

This is the codebase for Faucet, described in our manuscript: https://academic.oup.com/bioinformatics/article/34/1/147/4004871, by Roye Rozov, Gil Goldshlager, Eran Halperin, and Ron Shamir

Efficient Sequential and Batch Estimation of Univariate and Bivariate Probability Density Functions and Cumulative Distribution Functions along with Quantiles (Univariate) and Nonparametric Correlation (Bivariate)

This repository contains all the solutions of assignments, starter files and other materials related to this specialization.

A simple, time-tested, family of random hash functions in Python, based on CRC32 and xxHash, affine transformations, and the Mersenne Twister. 🎲

Create MPEG2-TS encapsulated stream-segments.

Python-Wrapper for Francesco Parrella's OnlineSVR C++ implementation with scikit-learn-compatible interface.