Download JAR files tagged by metric with all dependencies

This agent supports Instrumental custom metric monitoring for Java applications. It provides high-data reliability at high scale, without ever blocking your process or causing an exception.

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

This Java Toolbox provides several algorithms to align two input sequences, where your sequential data is allowed to be multimodal and multidimensional. We also provide additional tools to inspect the alignment results in more detail or even calculate derivatives of the alignment w.r.t. metric parameters such that you can optimize the alignment parameters according to some cost function. It is written in Java 1.7. and is also compatible with Matlab (version 2013b or higher).

Java client library for the New Relic REST APIs built using Jersey and Gson. The library includes over 110 operations across all of the available 35 New Relic services. It can be used by applications to automate the configuration of New Relic Monitoring, Alerting and Dashboards, but can also be used for extracting incident and metric data, executing Insights queries, and uploading plugin metrics.

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.