RaimaDB
RaimaDB is an embedded time series database for IoT and Edge devices that can run in-memory. It is an extremely powerful, lightweight and secure RDBMS. Field tested by over 20 000 developers worldwide and has more than 25 000 000 deployments.
RaimaDB is a high-performance, cross-platform embedded database designed for mission-critical applications, particularly in the Internet of Things (IoT) and edge computing markets. It offers a small footprint, making it suitable for resource-constrained environments, and supports both in-memory and persistent storage configurations. RaimaDB provides developers with multiple data modeling options, including traditional relational models and direct relationships through network model sets. It ensures data integrity with ACID-compliant transactions and supports various indexing methods such as B+Tree, Hash Table, R-Tree, and AVL-Tree.
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Interfacing Integrated Management System (IMS)
Interfacing’s Integrated Management System (IMS) is an AI-powered platform that unifies BPM, QMS, Document Control, and GRC into one platform. Organizations use IMS to model and automate processes, control documents, manage risks, and maintain regulatory compliance with full traceability and audit readiness.
Built for highly regulated sectors such as aerospace, life sciences, finance, and government, IMS provides real-time visibility, automated workflows, and AI-driven insights that improve quality and reduce operational risk. The platform is ISO 27001 certified and fully validated for 21 CFR Part 11, making it suitable for mission-critical environments requiring strong governance, security, and control. IMS also includes low-code automation, process mining, audit management, training tracking, CAPA workflows, and dashboards to help teams streamline operations and continuously improve. AI strengthens governance, improves accuracy, and reinforces regulatory control.
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alvaDesc
alvaDesc is a cheminformatics software for the calculation and analysis of molecular descriptors, fingerprints, and structural patterns for QSAR, QSPR, read-across, and machine learning applications. It computes more than 5,000 molecular descriptors (0D–3D), including constitutional, topological, geometrical, electronic, physicochemical, and fragment-based descriptors.
The software also generates molecular fingerprints and structural pattern counts for similarity analysis, clustering, and classification. Integrated tools support descriptor filtering and correlation analysis for robust and reproducible modeling.
alvaDesc integrates seamlessly with KNIME and Python, enabling efficient connection to external data analysis and machine learning workflows. It is widely used in academic and industrial research and supported by extensive documentation and scientific publications.
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