Open Source Linux Big Data Tools

Standard IT tools applied to SCADA/IoT (MongoDB, PostgreSQL/TimescaleDB,Node.js, C#, Golang, Grafana, etc.). MongoDB as the real-time core database, persistence layer, config store, SOE historian. Portability and interoperability over Linux, Windows, x86/64, ARM. Horizontal scalability, from a single computer to big clusters (MongoDB-sharding), Bare Metal, Docker containers, VM, cloud, or hybrid deployments. Unlimited tags, servers, and users. HTML5 Web interface. UTF-8/I18N. Protocols: IEC61850 Client, IEC60870-5-101/103/104 Client and Server, DNP3 Client, ICCP/TASE2, OPC-UA Client/Server, Modbus, MQTT/Sparkplug-B, Telegraf (various data sources for monitoring like CPU, SNMP, etc.), MCP server. Github project https://github.com/riclolsen/json-scada Requirements for Windows Installer: Windows 10/11 64 bits or Server 2016, Windows PowerShell.

Augustus development has moved to google code. The new project page is augustus.googlecode.com. New releases of the project are not currently being released to sourceforge. Augustus is designed for statistical and data mining models and produces and consumes models with 10,000s of segments. Versions of Augustus support PMML 3, 4.0.1, and 4.1.

This is the proof-of-concept implementation of the FAUM Clustering method. This implementation was used to perform the published results and is now released in the hope that it will be useful.

===== Project moved to https://github.com/fpetitjean/Chordalysis ===== Log-linear analysis is the statistical method used to capture multi-way relationships between variables. However, due to its exponential nature, previous approaches did not allow scale-up to more than a dozen variables. We present here Chordalysis, a log-linear analysis method for big data. Chordalysis exploits recent discoveries in graph theory by representing complex models as compositions of triangular structures, also known as chordal graphs. Chordalysis makes it possible to discover the structure of datasets with thousands of variables on a standard desktop computer. Associated papers at ICDM 2013, ICDM 2014 and SDM 2015 can be found at http://www.francois-petitjean.com/Research/ YourKit is supporting Chordalysis open source project with its full-featured Java Profiler. YourKit is the creator of innovative and intelligent tools for profiling Java and .NET applications. http://www.yourkit.com

HSRA is a MapReduce-based parallel tool for mapping reads from RNA sequencing (RNA-seq) experiments. RNA-seq analyses typically begin by mapping reads to a reference genome in order to determine the location from which the reads were originated, which is a very time-consuming step. This tool allows bioinformatics researchers to efficiently distribute their mapping tasks over the nodes of a cluster by combining a fast multithreaded spliced aligner (HISAT2) with Apache Hadoop, which is a distributed computing framework for scalable Big Data processing. HSRA currently supports single-end and paired-end read alignments from FASTQ/FASTA datasets. Moreover, our tool uses the Hadoop Sequence Parser (HSP) library (link above) to efficiently read the input datasets stored on the Hadoop Distributed File System (HDFS), being able to process datasets compressed with Gzip and BZip2 codecs.

MarDRe is a de novo MapReduce-based parallel tool to remove duplicate and near-duplicate DNA reads through the clustering of single-end and paired-end sequences from FASTQ/FASTA datasets. This tool allows bioinformatics to avoid the analysis of not necessary reads, reducing the time of subsequent procedures with the dataset. MarDRe is the Big Data counterpart of ParDRe (link above), which employs HPC technologies (i.e., hybrid MPI/multithreading) to reduce runtime on multicore systems. Instead, MarDRe takes advantage of the MapReduce programming model to significantly improve ParDRe performance on distributed systems, especially on cloud-based infrastructures. Written in pure Java to maximize cross-platform compatibility, MarDRe is built upon the open-source Apache Hadoop project, the most popular distributed computing framework for Big Data processing.

This project is dedicated to open source data quality and data preparation solutions. Data Quality includes profiling, filtering, governance, similarity check, data enrichment alteration, real time alerting, basket analysis, bubble chart Warehouse validation, single customer view etc. defined by Strategy. This tool is developing high performance integrated data management platform which will seamlessly do Data Integration, Data Profiling, Data Quality, Data Preparation, Dummy Data Creation, Meta Data Discovery, Anomaly Discovery, Data Cleansing, Reporting and Analytic. It also had Hadoop ( Big data ) support to move files to/from Hadoop Grid, Create, Load and Profile Hive Tables. This project is also known as "Aggregate Profiler" Resful API for this project is getting built as (Beta Version) https://sourceforge.net/projects/restful-api-for-osdq/ apache spark based data quality is getting built at https://sourceforge.net/projects/apache-spark-osdq/

The Universal Java Matrix Package (UJMP) is an open source Java library which provides sparse and dense matrix classes, as well as a large number of calculations for linear algebra such as matrix multiplication or matrix inverse. Operations such as mean, correlation, standard deviation, replacement of missing values or the calculation of mutual information are supported, too. The Universal Java Matrix Package provides various visualization methods, import and export filters for a large number of file formats, and even the possibility to link to JDBC databases. Multi-dimensional matrices as well as generic matrices with a specified object type are supported and very large matrices can be handled even when they do not fit into memory.