Search Results for "data modeling"

JetLinks Community is an open-source enterprise IoT platform built with Java, Spring Boot, WebFlux, Netty, Vert.x, Reactor, and related reactive technologies. It is designed to help teams quickly build IoT business systems without starting from a blank backend. The platform supports unified device modeling, unified device access, and centralized management across different device types, vendors, and communication protocols. It can connect devices through TCP, UDP, MQTT, HTTP, TLS, DTLS, and other protocol patterns while hiding much of the complexity of network programming. JetLinks also includes real-time data processing, device alerts, message notifications, data forwarding, geographic features, visualization, and a configurable rule engine. ...

OpenDDS is an open source implementation of the Object Management Group (OMG) Data Distribution Service (DDS), providing a publish/subscribe middleware solution for real-time distributed systems. OpenDDS includes development and run-time tools. Full product information, source code, documentation, build instructions, and license information are available from http://www.opendds.org. Commercial consulting, support, and training for OpenDDS are available. OpenDDS is in production use...

...It walks readers through the architecture, concepts, and engineering stack behind IoT systems instead of only listing isolated tools. The guide covers topics such as IoT market context, device models, ThingsBoard source-code learning, MQTT broker setup, CoAP services, message peak shaving, data modeling, and database selection. It is intended for developers who want to learn how real IoT platforms organize devices, messages, protocols, and backend services. The repository is especially useful as a structured study path for readers preparing to build or customize an IoT platform. It functions more as a technical tutorial and roadmap than as a single installable software package.

Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible...