Group: io.github.luzzu - All Dependencies

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Luzzu Semantics Module · Semantics is the underlying semantics description module for Luzzu.

Oct 27, 2018
7 usages
4 stars
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Luzzu Low-Level Operations Module · The low-level operations module consists of classes that support the execution and maintanace of Luzzu such as caching, filelocks, exception handling etc…

Oct 27, 2018
7 usages
4 stars
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Oct 27, 2018
2 usages
4 stars
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luzzu-webapp 4.0.0

Oct 27, 2018
1 usages
4 stars
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luzzu-qml 4.0.0

Luzzu Quality Metric Language · The Luzzu Quality Metric Language (LQML) is a simple declerative language which allows users of the Luzzu Framework to define their own metrics in a simple declerative manner.

Oct 27, 2018
1 usages
4 stars
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Luzzu Linked Data Quality Metric Commons · In this module we capture a number of data types and algorithms that can be potentially used for building Linked Data quality metrics. These are automatically included with the luzzu-assessment project, therefore, there is no need to import this dependency in your quality metric implementation project.s

Oct 27, 2018
1 usages
4 stars
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luzzu-io 4.0.0

Luzzu IO Module · This module describes the IO operations for the Quality Assessment Framework

Oct 27, 2018
1 usages
4 stars
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Luzzu High-Level Operations Module · This module consists of classes that support Luzzu on a higher level such as ranking

Oct 27, 2018
1 usages
4 stars
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Oct 27, 2018
1 usages
4 stars
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Luzzu Communications Module · This module deals with the RESTful APIs

Oct 27, 2018
4 stars
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luzzu 4.0.0

Luzzu · 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.

Oct 27, 2018
8 stars

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