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Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.

Luzzu is a Quality Assessment Framework for Linked Open Datasets. It is a generic framework based on the Dataset Quality Ontology (daQ), allowing users to define their own quality metrics. Luzzu is an integrated platform that: - assesses Linked Data quality using a library of generic and user-provided domain specific quality metrics in a scalable manner; - provides queryable quality metadata on the assessed datasets; - assembles detailed quality reports on assessed datasets. Furthermore, the infrastructure: - scales for the assessment of big datasets; - can be easily extended by the users by creating their custom and domain-specific pluggable metrics, either by employing a novel declarative quality metric specification language or conventional imperative plugins; - employs a comprehensive ontology framework for representing and exchanging all quality related information in the assessment workflow; - implements quality-driven dataset ranking algorithms facilitating use-case driven discovery and retrieval.

EasyConfig provides simple way to overview and apply settings to file or folder based collections of files. Synonyms to "setting" are property, attribute, value while throughout application "setting" is used. The settings are groupped in "configuration" that is collection of settings from various sources. Main design concepts are: * minimalistic way to describe configuration * pluggable support for data types (validation), setting sources, source handlers Sample use case: An application is deployed in multiple locations. We need to quickly check key settings/parameters and optionally modify some of them. These values are located in different places: - in files directly in file structure - in files inside archive files (optionally nested archives) - values in DB tables - values accessible via URLs - other sources (just guessed: SSH/telnet connection+some command(s), UPnP devices, proprietary protocols, etc) We gather info from any supported (extendable) source and can modify and apply changes if supported by source (e.g. we can't update value that is count of rows in DB table, but we can read that value).