Awesome GitHub RepositoriesMulti-Dimensional Analysis

StarRocks is a distributed SQL OLAP database engine designed for real-time analytics and high-performance multi-dimensional analysis. It functions as a data lakehouse query engine that enables SQL execution across large datasets and external open table formats without requiring local data imports. The system employs a shared-nothing distributed architecture and utilizes the MySQL protocol to integrate with business intelligence tools. It maintains real-time data consistency through a primary key upsert model and accelerates query response times using vectorized execution and cost-based optimi

Boost is a collection of portable, high-performance source libraries that extend the C++ standard library. It provides a wide range of reusable components, data structures, and algorithms designed to add capabilities to the base language across different platforms. The project is distinguished by its extensive focus on compile-time template metaprogramming and generic programming. It implements advanced architectural patterns such as policy-based design, concept-based type validation, and the use of SFINAE for conditional template resolution to minimize runtime overhead. The library covers a

dc.js is a multi-dimensional analysis tool and visualization framework used to build interactive data dashboards. It functions as a charting library that renders diverse SVG visualizations powered by D3 and integrates natively with Crossfilter to enable coordinated filtering across large datasets. The project is distinguished by its linked-view coordination, where selecting a data range or category in one chart simultaneously updates all other connected views. This allows for dynamic data exploration through dimensional chart linking and coordinated brushing, transforming raw datasets into na

GrowthBook is a feature flagging and experimentation platform that utilizes a warehouse-native approach to data analysis. It serves as a system for managing feature rollouts and conducting A/B tests by executing SQL queries directly against existing data warehouses to calculate experiment results. The platform is distinguished by its integration of a Model Context Protocol server, which allows AI coding assistants and IDEs to manage flags and query analytics using natural language. It also provides specialized capabilities for AI model optimization, enabling the testing of prompts and models

Pinot is a distributed, columnar analytical database designed for high-concurrency, low-latency query processing. It functions as a real-time OLAP datastore, enabling interactive, user-facing analytics by ingesting and querying massive datasets from both streaming and batch sources. The system architecture relies on a centralized controller for cluster coordination and a distributed segment-based storage model to ensure horizontal scalability. The platform distinguishes itself through a hybrid ingestion pipeline that unifies real-time event streams and historical batch data into a single quer

Prometheus client_golang is the official Go client library for instrumenting applications with Prometheus metrics. It provides a metric registry that manages and exposes custom application metrics like counters, gauges, histograms, and summaries in Prometheus format for HTTP scraping by a Prometheus server. The library also includes a remote read client that sends PromQL queries to a Prometheus server over HTTP and retrieves time series data programmatically. The library supports creating separate registries to isolate metric namespaces and control which metrics are exposed per scrape endpoin

Dieses Projekt ist eine Machine-Learning-Bibliothek, die eine Sammlung von Implementierungen für überwachte und unüberwachte Lernalgorithmen bereitstellt. Sie dient als Deep-Learning-Framework, als Sammlung statistischer Klassifikatoren und als Suite von Werkzeugen für unüberwachtes Lernen und Dimensionsreduktion. Die Bibliothek ermöglicht die Konstruktion neuronaler Netze, einschließlich Multi-Layer-Perzeptrons und Convolutional Networks für die Mustererkennung. Sie bietet zudem Werkzeuge zur Durchführung von Hauptkomponentenanalysen (PCA) und Manifold-Learning zur Visualisierung hochdimensionaler Datensätze sowie eine Suite von Clustering-Algorithmen, die unbeschriftete Daten durch iterative Partitionierung gruppieren. Das Projekt deckt ein breites Spektrum an prädiktiven Modellierungsfunktionen ab, einschließlich Klassifizierungs- und Regressionsaufgaben unter Verwendung von Entscheidungsbäumen, k-nächste-Nachbarn, Bayes-Klassifikatoren, Support Vector Machines und Ridge-Regression. Es enthält zudem Werkzeuge für Bildklassifizierungs-Workflows und die Analyse unbeschrifteter Daten.

Lucid ist ein TensorFlow-Interpretierbarkeits-Toolkit und eine Visualisierungsbibliothek, die darauf ausgelegt ist, die internen Repräsentationen neuronaler Netze zu analysieren. Sie fungiert als gradientenbasiertes Optimierungs-Framework, das Bilder und Atlanten generiert, um die von spezifischen Neuronen und Schichten gelernten Features offenzulegen. Die Bibliothek ermöglicht die Erstellung von Aktivierungsatlanten und das Mapping hochdimensionaler neuronaler Aktivierungen in niedrigdimensionale Räume, um das Modellverhalten zu untersuchen. Sie nutzt differenzierbare Bildparametrisierung, um visuelle Inputs zu optimieren, die Netzwerkkomponenten maximal aktivieren. Das System deckt ein breites Spektrum an Interpretierbarkeits-Infrastruktur ab, einschließlich Aktivierungsverteilungs-Mapping und Feature-Visualisierungs-Forschung. Es enthält zudem Utilities zum Importieren vortrainierter Modelle und zum Persistieren von Netzwerkgewichten für laufende Analysen.

Yellowbrick is a machine learning visualization library and model diagnostic tool designed to analyze feature importance, target distributions, and model error metrics. It serves as a visual toolkit for diagnosing underfitting and overfitting through the use of validation and learning curves. The project provides specialized suites for evaluating predictive models and unsupervised learning. It enables the determination of optimal cluster counts via elbow methods and silhouette coefficients, and assesses classifier and regressor quality through ROC curves, confusion matrices, and residual plot

This is a Prometheus Python client library used for instrumenting Python applications. It provides the tools necessary to record counters, gauges, and histograms within a process to monitor application health and expose that data as a Prometheus exposition format provider. The library enables cloud native observability by allowing developers to define custom telemetry and track internal application events. It transforms internal application data into a standardized text format required by Prometheus scrapers for collection. The project covers a variety of monitoring and observability capabil

Stumpy ist eine Python-Bibliothek für skalierbare Zeitreihenanalyse, die sich auf die Implementierung von Matrix-Profile-Algorithmen konzentriert. Sie bietet ein Framework zur Berechnung von Distanzprofilen, um wiederkehrende Muster und Anomalien innerhalb von Zeitreihendaten zu identifizieren. Das Projekt zeichnet sich durch seine Fähigkeit aus, rechenintensive Aufgaben über GPU-Hardware und verteilte Cluster mittels Dask zu skalieren. Es unterstützt multidimensionale Analysen zur Entdeckung von Motiven über gleichzeitige Datenströme hinweg und bietet inkrementelle Berechnungen für Echtzeit-Streaming-Analysen. Die Bibliothek deckt ein breites Spektrum an Zeitreihen-Mining-Techniken ab, einschließlich Motiv-Entdeckung, Anomalieerkennung und Sequenz-Musterabgleich. Sie bietet zudem Tools für semantische Segmentierung zur Erkennung von Regime-Änderungen und die Extraktion zeitlich geordneter Ketten ähnlicher Subsequenz-Muster.

pytorch-fid is a PyTorch-based evaluator and image distribution analysis library used to calculate the Fréchet Inception Distance. It functions as a benchmarking tool that maps image pixels to high-dimensional feature vectors using a pre-trained convolutional neural network to measure the mathematical divergence between real and synthetic datasets. The library quantifies the quality and diversity of generative models by representing image feature sets as mean and covariance matrices. It allows for the extraction of latent representations from specific neural network layers, with configurable

Kylin is a distributed OLAP engine designed for executing fast SQL queries on massive datasets. It utilizes multi-dimensional data cubes to pre-calculate data aggregates, enabling sub-second response times for large-scale analytical queries and big data analytics. The system focuses on large-scale data warehousing and multi-dimensional data modeling. It allows for the organization and querying of vast amounts of structured data to support business intelligence and reporting workflows through distributed SQL querying.

POT is an optimal transport library providing a collection of solvers for computing Wasserstein, Gromov-Wasserstein, and Fused Gromov-Wasserstein distances between probability distributions. It functions as a differentiable tensor framework that integrates with various tensor libraries to enable automatic differentiation and GPU acceleration. The project is distinguished by its ability to align data distributions across different metric spaces by comparing internal relational structures rather than coordinates. It implements mathematical optimization algorithms as differentiable layers, allow