Open Source Windows Big Data Tools - Page 3
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Big Data Tools for Windows
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Outbound sales softwareAdversus is an outbound dialing solution that helps you streamline your call strategies, automate manual processes, and provide valuable insights to improve your outbound workflows and efficiency.
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BEAR
CBR Meets Big Data
Case-based regression learner for big data. The package contains source and binary files for running BEAR's method. BEAR utilizes EAR4 and locality sensitive hashing in its implementation. -
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Big Sack
Big Sack: A lightweight Java Key/Value store with undo and disk cache.
Big Sack is a Java persistence mechanism that allows storage of key value pairs following the popular Big Data paradigms. Its a very simple and straightforward way to bridge the gap between in-memory data structures and long-term storage. It has the convenience of Java SDK TreeMap and TreeSet classes and is used the same easy way, but it includes rollback through undo logging to checkpoint data so it does not wind up in an unknown state regardless of failures. Data storage in the exabyte range is possible using filesystem and/or memory-mapped IO. Three levels of configurable write-through caching at different granularities ensure performance. -
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Chordalysis
Log-linear analysis (data modelling) for high-dimensional data
===== 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 -
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Custom Apache Big data Distribution
A Custom Apache Distribution including Spark and Hadoop, for Windows.
This Distribution has been customized to work out of the box. So, just download it, and unzip it. Set the Path variables for bin folders, HADOOP_HOME, SPARK_HOME, and JAVA_HOME. That's it..! use Hadoop and Spark natively on Windows. -
Comet Backup - Fast, Secure Backup Software for MSPsComet is a flexible backup platform, giving you total control over your backup environment and storage destinations.
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DSTK - DataScience ToolKit
DSTK - DataScience ToolKit for All of Us
DSTK - DataScience ToolKit is an opensource free software for statistical analysis, data visualization, text analysis, and predictive analytics. Newer version and smaller file size can be found at: https://sourceforge.net/projects/dstk3/ It is designed to be straight forward and easy to use, and familar to SPSS user. While JASP offers more statistical features, DSTK tends to be a broad solution workbench, including text analysis and predictive analytics features. Of course you may specify JASP for advanced data editing and RapidMiner for advanced prediction modeling. DSTK is written in C#, Java and Python to interface with R, NLTK, and Weka. It can be expanded with plugins using R Scripts. We have also created plugins for more statistical functions, and Big Data Analytics with Microsoft Azure HDInsights (Spark Server) with Livy. License: R, RStudio, NLTK, SciPy, SKLearn, MatPlotLib, Weka, ... each has their own licenses. -
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FrincBackup
Incremtal backup tool supporting removable storage devices
FrincBackup means free incremental backup. It is developed for backing up a x TB NAS with storage devices in a logical volume to multiple removable storage devices, such as 500 GB USB hard drives. Files are backuped as files (not as an archive) and are readable without the need of a tool and without the need of FrincBackup itself (allthough there is a restore mode for better handling). -
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GOBIG is a toolbox that can be used for detecting genetic variations. The project is intended to handle big data. What's more important is that it be used to detect clusters of SNP variants. It is the intention to use the toolbox with common and rare variants. To use it, for example, to find the genetic map of genes causing complex diseases.
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LEACrypt
TTAK.KO-12.0223 Lightweight Encryption Algorithm Tool
The Lightweight Encryption Algorithm (also known as LEA) is a 128-bit block cipher developed by South Korea in 2013 to provide confidentiality in high-speed environments such as big data and cloud computing, as well as lightweight environments such as IoT devices and mobile devices. LEA is one of the cryptographic algorithms approved by the Korean Cryptographic Module Validation Program (KCMVP) and is the national standard of Republic of Korea (KS X 3246). LEA is included in the ISO/IEC 29192-2:2019 standard (Information security - Lightweight cryptography - Part 2: Block ciphers). This project is licensed under the ISC License. Copyright © 2020-2021 ALBANESE Research Lab Source code: https://github.com/pedroalbanese/leacrypt Visit: http://albanese.atwebpages.com -
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MapReduce Brazil
Aggregates MapReduce projects
Nowadays the production and storage of Big Data is common, both in the academy and in the enterprises. To process this huge amount of data it is essential the use of high performance platforms and programming models like MapReduce -
Get full visibility and control over your tasks and projects with Wrike.Wrike offers world-class features that empower cross-functional, distributed, or growing teams take their projects from the initial request stage all the way to tracking work progress and reporting results.
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Modin
Scale your Pandas workflows by changing a single line of code
Scale your pandas workflow by changing a single line of code. Modin uses Ray, Dask or Unidist to provide an effortless way to speed up your pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless integration and compatibility with existing pandas code. Even using the DataFrame constructor is identical. It is not necessary to know in advance the available hardware resources in order to use Modin. Additionally, it is not necessary to specify how to distribute or place data. Modin acts as a drop-in replacement for pandas, which means that you can continue using your previous pandas notebooks, unchanged, while experiencing a considerable speedup thanks to Modin, even on a single machine. Once you’ve changed your import statement, you’re ready to use Modin just like you would pandas. -
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Nebula Graph
A distributed, fast open-source graph database
The graph database built for super large-scale graphs with milliseconds of latency. Optimized SUBGRAPH and FIND PATH for better performance. Optimized query paths to reduce redundant paths and time complexity. Optimized the method to get properties for better performance of MATCH statements. Nebula Graph adopts the Apache 2.0 license, one of the most permissive free software licenses in the world. Free as in freedom, because, under the Apache 2.0 license, you can use, copy, modify and redistribute Nebula Graph, even for commercial purposes, all without asking for permission. We believe that great open source projects are not built in isolation, but rather by a community of contributors. We welcome contributions to Nebula Graph from anyone regardless of skill level or background in software development. If you have an idea for a feature you would like to see added, or you have identified a bug that needs fixing, please don't hesitate to submit an issue to our Github repository. -
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Neuro
The Neuro crypto currency
The Neuro NRO cryptocurrency is designed to support solutions of machine learning tasks, big data and neural networks. Neuro is a scientific-technical project uniting scientists, engineers and programmers inspired by the idea to build something big, kind and bright. From the first stages of work, we will be engaged in the development of new architectures and algorithms of neural networks. Someday we will undoubtedly enter the annual ImageNet Challenge contest to compete with such giants as GoogLeNet Inception and Microsoft ResNet. At further stages of the work, we adapt the neural networks to calculate molecular interactions in protein environments. Our system will help to look for new types of drugs for cancer, Alzheimer's and other serious problems of modern medicine. We plan to make a serious contribution to the increase of human life expectancy. -
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OCW Test - Out of Commerce Works
Program for out of commerce works detection
The OCW Test program has been designed to provide assistance in the detection of works outside trade, taking as reference a list of works from a specific bibliographic catalog. In this first version, the program operates on the identifiers of the books of the library of the Complutense University of Madrid. However, the program can be reedited, to work on any bibliographic catalog. -
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ODD Platform
First open-source data discovery and observability platform
Unlock the power of big data with OpenDataDiscovery Platform. Experience seamless end-to-end insights, powered by unprecedented observability and trust - from ingestion to production - while building your ideal tech stack! Democratize data and accelerate insights. Find data that fits your use case and discover hints left by your peers to leverage existing knowledge. Explore tags, ownership details, links to other sources and other information to shorten and simplify data discovery phase. Forget unnerved stakeholders and wasting too much time on digging the root cause of data issues when it fails. With ODD’s automatic company-wide ingestion-to-product lineage you’ll have answers in just seconds and stakeholders won’t need to wait. Sleep well, knowing all your data is in check. Forget manual testing, days of debugging, and weeks of worrying. Know the impact of each code change with automatic testing. Enjoy lineage and alerts powered with data quality information. -
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Occursions
Fast customizable time series web database for big data like log files
Our goal is to create the world's fastest extendable, non-transactional time series database for big data (you know, for kids)! Log file indexing is our initial focus. For example append only ASCII files produced by libraries like Log4J, or containing FIX messages or JSON objects. Occursions was built by a small team sick of creating hacks to remotely copy and/or grep through tons of large log files. We use it to index around a terabyte of new log data per day. You can use it too. Who doesn't have `just too many' log files? Occursions asynchronously tails log files and indexes the individual lines in each log file as each line is written to disk so you don't even have to wait for a second after an event happens to search for it. Occursions uses custom disk backed data structures to create and search its indexes so it is very efficient at using CPU, memory and disk. You can extend Occursions with shared libraries to support your own file formats, even binary file formats! -
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Open Source Data Quality and Profiling
World's first open source data quality & data preparation project
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/ -
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PROPER is a package for visual evaluation of ranking classifiers for biological big data mining studies in the mathematical language MATLAB. It is an efficient tool for optimization and comparison of the state-of-the-art ranking classifiers by generating over 20 different high quality two- and three-dimensional performance curves.
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R Hadoop for Big Data
Download Free Associated R open source script files for big data analy
Download Free Associated R open source script files for big data analysis with Hadoop and R These are R script source file from Ram Venkat from a past Meetup we did at http://www.meetup.com/R-Matlab-Users/events/85160532/ Also, there is a long video and Powerpoint presentation slide PDF with R files at: http://quantlabs.net/blog/2012/11/how-to-use-hadoop-and-r-for-big-data-parallel-processing-free-download-pdf/ Download source files from http://quantlabs.net/blog/2012/11/download-free-associated-r-open-source-script-files-for-big-data-analysis-with-hadoop-and-r-rstats-hadoop/ -
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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data with large size. -
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Random Bits Regression
Random Bits Regression is a strong general predictor.
We proposed an accurate, robust and fast general predictor (RBR) for regression and classification in big data era. The application of this method is very broad, from science to industry, finance and health. The accuracy and robustness improvement of our method over existing method could bring huge benefits in some critical applications. For example, natural disaster prediction, stock price prediction, personal/population disease prediction. The fast-speed nature of our method not only allows big data analysis but also enables real-time recognition and predictions. The RBR framework also hints the mechanism of brain function and leads to a "wide learning" hypothesis. We believe that this method will make a great impact and enable many downstream applications. -
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Redis Desktop Manager
:wrench: Cross-platform GUI management tool for Redis
Redis Desktop Manager is a fast, open source Redis database management application based on Qt 5. It's available for Windows, Linux and MacOS and offers an easy-to-use GUI to access your Redis DB. With Redis Desktop Manager you can perform some basic operations such as view keys as a tree, CRUD keys and execute commands via shell. It also supports SSL/TLS encryption, SSH tunnels and cloud Redis instances, such as: Amazon ElastiCache, Microsoft Azure Redis Cache and Redis Labs. -
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Relation Tags
Source code for be able to use Relation Tags.
Source code for be able to use Relation Tags. It is part of project VocabularyMem but can be used separately. Relation Tags are tags which can be relationed together . For example tag "Paris" and tag "France" can be relationed with a relation "is part of". This code is created from 0 and is able to define which type of relation we use, using most elemental mathematic properties. It is strongly recommended to read "Relation Tags guide for programmers". Inside source zip, also contains dialogs for set properties of this extended tags. All this dialogs files finish either with "...dlg.cpp" or ",,,dlg.h". Please read "readme" file. It is recommended to use a binary matrix class like BinMatrix in order to have enough speed for calculations of implicit relations in a system of bogus tags with big data. Need to be compiled with C++11 and Qt libraries -
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Sample Level Musical Timeline
Sample Level Modulation of Musical Timeline
Sample Level Modulation of Musical Timeline Mingfeng Zhang Dept. of Electrical and Computer Engineering, University of Rochester In this toolbox we provide signal processing tools to allocate music events (samples of musical notes) to specified time locations with sample level accuracy. In this implementation, we use computational tools to add in micro-timing variations in J.S. Bach four-part chorales as a "visualizer" for big data. By extracting data patterns from multiple time scales, we implement a tool that musicians can perform the big data at different resolutions. This toolbox will need the following supporting toolboxes: MIDI TOOLBOX https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/miditoolbox MIR TOOLBOX https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox Please add the path in MATLAB for these two toolbox. Please also read the project document file (readme.doc/pdf) for more details -
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SentimentAnalysis-Rick&Morty
Rick & Morty Sentiment Analysis - End-of-Degree Project - UNIR
The remarkable progress in the field of Big Data has driven the development of new technologies in natural language processing and data analysis. Text mining is a fascinating application of data analysis that extracts relevant information from related writings in different linguistic contexts. And therefore, in natural language processing, sentiment analysis and classification stands out as a key application supported by text mining. Through the extraction of information from textual data, it becomes possible to identify and comprehend the sentiments and emotions conveyed. In this end-of-degree work, we analyze and classify the dialogue of characters in an English-language television series as "Rick and Morty" using Python. The objective is to identify and categorize the feelings and emotions expressed in the text, comparing the human perception of the characters' personalities with the results obtained using natural language processing techniques. -
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The intellectual system of Eidos
http://lc.kubagro.ru/
http://lc.kubagro.ru/ http://lc.kubagro.ru/aidos/index.htm http://lc.kubagro.ru/aidos/_Aidos-X.htm On the IBM PC, the Eidos system started working in 1992. MS Windows has been running since 2012. Implemented in Alaska+Express. I want to try to translate some modes, and maybe all of them, to the Harbor. The full source text in a single file is here: http://lc.kubagro.ru/__AIDOS-X.txt Responsible Secretary Kubgau scientific journal, Professor of computer science Department Kubgau technologies and systems, doctor of Economics, candidate of technical Sciences, Professor E. V. Lutsenko http://lc.kubagro.ru/ http://ej.kubagro.ru/ https://www.researchgate.net/profile/Eugene_Lutsenko https://www.facebook.com/groups/558866657885969/ Quick free publication of articles in the RSCI with DOI: http://lc.kubagro.ru/ResearchGate.doc
