Open Source Windows Data Analytics Tools - Page 5
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Data Analytics Tools for Windows
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Framework for text mining, data integration and data analysis. Keywords: ontology and graph alignment, relation mining, warehouse, semantic database integration, bioinformatics, systems biology, microarray, Java.
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PAIDA is pure Python scientific analysis package and supports AIDA (Abstract Interfaces for Data Analysis). PAIDA can create/plot histograms and functions etc. The parameter optimization and its error evaluation are also supported. Can use with Jython!
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PL-DAS
Port Logistics Data Analysis System using Process Mining
This project is for analyzing operational data from container terminal, applying process mining and port logistics domain knowledge. This project is found by Total Soft Bank, Ltd. and Busan National University, based on ProM framework (http://www.promtools.org) which is also an open source project. This project is sponsored by NIPA (National IT Industry Promotion Agency), Korea. -
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QSAS plug-ins
Plug-ins for the space physics data analysis software QSAS.
The project is concerned with developing new plug-ins for the space plasma analysis software QSAS. The plug-ins are developed mainly for the Cluster II mission, Champ and Swarm. QSAS can be downloaded from: http://www.sp.ph.ic.ac.uk/csc-web/QSAS/ -
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QuickNXS
Polarized ToF reflectivity raw data analysis tool
Data evaluation tool for the magnetism reflectometer at the spallation neutron source (BL-4A@SNS). Reads raw nexus files (HDF5) of histogrammed or event mode data to create reflectivity curves and 2D Q-maps. -
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SCiDA Pro
Program for solar cell production data analysis
The purpose of the SCiDA Pro program is to help with processing solar cell production data. It has the following features: - Able to handle large data sets in a fast way (e.g. plotting 100k cell data takes a few seconds) - Easy data filtering - Easy generation of a data summary report - Extensive data plotting features - Cross-platform (Windows/Linux/MacOS) - Supports multiple languages -
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Command line encryption tool for one time, daemon, or stream data processing. Data stats, check sums, conversion to/from text. Data/keys from files, pipes, standard input. In-place/diverted processing or data-analysis-only. Random, file, password keys.
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i-Map - Plot Geolocation from Images
Automatically plots latitude, longitude from images on Google maps.
i-Map is a Photo metadata forensic tool for Geo-location analysis of images that are clicked from GPS enabled devices. In this tool, you can load 100s of images from a suspect's device and analyze them to know various locations where photos were clicked on mobile phone/tablet. After loading images, with a single click, iMap plots all the images on World Map to visually check where they have been captured, generate timeline and activity of suspect and match them with CDR (Call Detail Record) Details. To generate a report, you can export this data into PDF or Excel file according to your requirements. -
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jHepWork
jHepWork was a name of the DataMelt program in 2005-20013
jHepWork (2005-2013) was an environment for scientific computation, data analysis and data visualization for scientists, engineers and students. The program is fully multiplatform (100% Java) and integrated with the Jython (Python) scripting language. Currently the project is known under the name DataMelt (https://datamelt.org) -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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neural network designer
a dbms for neural nets. Chatbots, DTrees, random forests, n-grams,...
This project consists out of a windows based designer application and a library (that can run on multiple platforms, including android) together with several demo applications (including an MVC3 chatbot client and an android application). It is probably best compared to a database management system, but for neural networks instead of relational data. As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer, you are able to create different types of decision trees, random forests, n-grams, pattern-matchers, conversational agents and all sorts of AI related algorithms. You can combine statistical approaches as well as pattern matchers or others. Do natural language processing, image or data analysis & interpretation,... -
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.NET for Apache Spark
A free, open-source, and cross-platform big data analytics framework
.NET for Apache Spark provides high-performance APIs for using Apache Spark from C# and F#. With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. .NET for Apache Spark is compliant with .NET Standard - a formal specification of .NET APIs that are common across .NET implementations. This means you can use .NET for Apache Spark anywhere you write .NET code allowing you to reuse all the knowledge, skills, code, and libraries you already have as a .NET developer. .NET for Apache Spark runs on Windows, Linux, and macOS using .NET Core, or Windows using .NET Framework. It also runs on all major cloud providers including Azure HDInsight Spark, Amazon EMR Spark, AWS & Azure Databricks. -
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2P Toolbox
Two-Photon Data Analysis Toolbox
The 2P_Tools.jar is a series of hopefully useful Fiji Plugins to evaluate data acquired by two-photon imaging. It supports multiple features like conversion to normalized image stacks, preprocessing and image segmentation. -
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A More Accurate Fourier Transform
Computes Accurate Fourier Transform via Explicit Integration not FFT
Fourier transform methods are used to analyze functions and data sets to provide frequencies, amplitudes, and phases of underlying oscillatory components. Fast Fourier transform (FFT) methods offer speed advantages over evaluation of explicit integrals (EI) that define Fourier transforms. It has been shown recently that EI methods can be more accurate than FFT methods (Courtney and Courtney, A More Accurate Fourier Transform) for scientific data analysis and that EI methods can be implemented in reasonable times with modern computing power. This project makes the source code (and command line Windows executable) developed in that study openly available for scientific and engineering uses. -
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Automated Microarray Data Analysis
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Advanced Online Surveys for a sophisticated market research and other kinds of professional data collection by using the world wide web. The potential for charmelionic data analysis and aquisition of additional data distinguishes it from other products.
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Ahoy
Simple, powerful, first-party analytics for Rails
Ahoy is a first-party analytics library built primarily for Ruby on Rails, designed to let applications track visits and events in a clean, integrated way rather than relying on third-party tooling. It stores data in your own database by default, which gives developers full control over what data is captured, how it's processed, and how it’s used, sidestepping privacy concerns of external analytics providers. The library supports Rails, JavaScript, and native apps, making it flexible across front-end/back-end and mobile contexts. Because it’s designed for developers who already own their data stack, Ahoy encourages self-hosted analytics workflows, custom reporting, and integration with existing database infrastructure. It includes features for tracking visits (sessions), events (actions), and user properties, so you can introspect user journeys and behaviour within your application domain. -
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Amazon Kinesis Flink Connectors
Contains various Apache Flink connectors to connect to AWS data
This library contains various Apache Flink connectors to connect to AWS data sources and sinks. This repository contains various Apache Flink connectors to connect to AWS Kinesis data sources and sinks. Flink maintain backwards compatibility for the Sink interface used by the Firehose Producer. This project is compatible with Flink 1.x, there is no guarantee it will support Flink 2.x should it release in the future. An Apache Flink application is a Java or Scala application that is created with the Apache Flink framework. You author and build your Apache Flink application locally. Applications primarily use either the DataStream API or the Table API. The other Apache Flink APIs are also available for you to use, but they are less commonly used in building streaming applications. The Apache Flink DataStream API programming model is based on two components. -
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An introduction to Data Analysis in R
A guide for learning the basic tools on data anaylisis with R
An Introduction to Data Analysis in R [Book] A guide for learning the basic tools on data anaylisis: process, visualize and learn from your data using R programming. This repository holds the necessary data sets for the book "An introduction to Data Analysis in R", to be published by Springer series Use R!. The book can be purchased in XXX. The book is meant as an introductory guide to manipulate data sets in the Big Data paradigm. One of the main goals of this book is to take the analyst from the very first moment when she/he contacts with data to the final conclusion and presentation of results of analysis. We take into account the variety of fields where data analysis occurs nowadays. We pay special attention to the different ways to obtain data and how to make it manageable before starting the analysis. The data analysis includes most of the basic visualization options and some advanced extra options. Finally, basic statistics is used to learn from the processed data. -
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Apache InLong
Apache InLong - a one-stop integration framework for massive data
Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 80 trillion pieces of data per day) reporting services in big data scenarios. The entire platform has integrated 5 modules: Ingestion, Convergence, Caching, Sorting, and Management, so that the business only needs to provide data sources, data service quality, data landing clusters and data landing formats. -
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Audit Data Analytics, LLC's ADA software is an open source solution for performing operations on audit data, such as filtering specific records and summarizing data sets.
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BDS
Blockchain data parsing and persisting results
JD Cloud Blockchain Data Service (BDS) is a real-time data aggregating, analyzing, and visualization service for chain-like unstructured data from all kinds of 3rd party Blockchains. Splitter is the key module of Blockchain Data Service (BDS) and provides data analysis capability. Splitter is responsible for consuming blockchain data from message queue (kafka) and inserting data into persistent data storage services (relational database, data warehouse, etc.) for further processing. Before compiling and running BDS, you must install go's compilation environment locally. -
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BIL++ is a set of standalone C++ packages for data processing in Bioinformatics (Graph mining, Bayesian networks, Genetic algorithm, Discretization, Gene expression data analysis, Hypothesis testing).
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Bamformatics
Toolkit and GUI for sequencing data analysis
The Bamformatics project aims to provide a coherent and consistent approach to analysis of high-throughput sequencing data. Its toolkit includes, among others, programs to identify variants and to compute various types genomic tracks. It also provides a graphical user interface to facilitate general bioinformatic workflows. The project wiki contains further details. -
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The project would include the development of XUL, CSS and Javascript components to assist biologists in sequence manipulations, data mining, data analysis, etc.).
