Project details for QMiner

Logo QMiner 5.0.0

by blazfortuna - April 8, 2016, 14:17:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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QMiner is an open source analytics engine for performing large scale data analysis written in C++ and exposed as a Node.js module. QMiner is distinguished by five main design elements which focus on storage, online and real-time processing as well as fast prototyping and thus give QMiner unique advantages as a data analytics platform for processing streams of structured and unstructured data.

These design elements are incorporated in a five layer architecture represented by: 1) storage and indexing optimized for stream processing and feature extraction 2) stream processing operators which can be assembled into scalable pipelines 3) feature extractor that map raw data into richer feature vectors covering many applications (e.g. text, finance, sensors) 4) linear algebra using state-of-the-art numeric libraries for optimal performance (e.g. OpenBLAS, Intel MKL) 5) analytics modules covering various areas of machine learning: supervised and unsupervised learning, visualization and active learning.

QMiner can be used as a module in Node.js, enabling fast and easy prototyping of data analytics applications. The main benefit of the scripting layer is tight integration of complete verticals from data acquisition, storage, analytics to web services and user interaction within one Node.js application without incurring performance penalties.

Short example applications implemented in QMiner: - -

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows, Mac Os X
Data Formats: Csv, Any, Json
Tags: Supervised, Feature Extraction, Social Network Analysis, Stream Processing, Text Mining, Unsupervised
Archive: download here

Other available revisons

Version Changelog Date

Initial Announcement on

April 8, 2016, 14:17:58

Initial Announcement on

March 4, 2016, 12:38:08


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