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RDF-Centric Map/Reduce Framework and Freebase data conversion tool
The main features of paulhoule/infovore are: Graph Databases.
Projects with overlapping indexed features include: bitnine-oss/agensgraph — AgensGraph, a transactional graph database based on PostgreSQL. cayleygraph/cayley — Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model.… dgraph-io/dgraph — Dgraph is a distributed graph database designed to store and query highly connected data. It organizes information as… gchq/gaffer — A large-scale entity and relation database supporting aggregation of properties. kakao/actionbase — One database for likes, views, follows — pre-computed, served in real-time. arangodb/arangodb — This project is a multi-model database system designed to store and manage information as documents, graphs, and…
AgensGraph, a transactional graph database based on PostgreSQL
Cayley is a graph database engine designed for storing and querying interconnected data using a quad-based data model. It functions as an RDF quad store, managing information through subjects, predicates, objects, and labels. The system features a modular graph store architecture with pluggable backends, allowing it to swap between in-memory storage and various external persistent databases. It includes a GraphQL-inspired API and a dedicated data visualizer for the interactive exploration of nodes and edges. Query capabilities cover bidirectional path traversal and multi-syntax execution usi
Dgraph is a distributed graph database designed to store and query highly connected data. It organizes information as nodes and edges to represent complex relationships between entities, providing a platform for managing and analyzing deeply linked datasets. The system functions as a horizontally scalable cluster that partitions data across multiple nodes to maintain performance and availability as information volume increases. It utilizes a specialized query language built for low-latency navigation of interconnected data points, allowing for the execution of complex queries across large-sca
This project is a multi-model database system designed to store and manage information as documents, graphs, and key-value pairs within a single engine. It functions as a graph database and knowledge graph platform, providing the infrastructure to build, query, and visualize structured data models. By integrating vector search capabilities, the system serves as a vector database that supports retrieval-augmented generation for artificial intelligence applications. The platform distinguishes itself through a unified query language that allows users to perform document lookups, graph traversals