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Data Analytics
</h1>
<p class="text-white link-nav"><a href="/components-for-industry/application-services-and-middleware/data-management/">Data Management</a><span class="lnr lnr-arrow-right"></span><a href="/components-for-industry/application-services-and-middleware/data-management/data-analytics">Data Analytics</a></p>
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<h4>Import datasets</h4>
<p>
Easily import CSV-based datasets.
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<h4>Create Analytic Models</h4>
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Use your own datasets for creating personalised Analytics models.
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<h4>Train and test your Analytics model</h4>
<p>
Train the selected algorithm and test it with your imported datasets.
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<h4>Publish to vf-Store</h4>
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Once ready, easily, and quickly publish to vf-Store.
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<h2>Data Analytics</h2>
<br>
<p>
The vf-OS Data Analytics covers the creation of building blocks for off-line and stream analytical processing of inputs
coming from the Manufacturing environment. This includes machine learning algorithms supporting supervised and unsupervised
scenarios.
</p>
<p>
In a factory there will be two different types of analytics tasks to be performed: those ones depending on historical data sources,
e.g. readings of sensors for a given period of time in the past, and those ones that need to be calculated on real-time as they will
be involved in triggering alarms conducting to time (or spend) critical actions. As such, the Data Analytics component integrates these
two different modules focusing each of them in these two areas.
</p>
<p>
The Historic Data Analytics is a browser-based tool designed to guide the developer in specifying and deploying historic analytics models.
The Streaming Data Analytics, though, is a service that detects anomalies in the values received from streaming data. Both of them are
callable through the vf-OS Studio.
</p>
<p>
In the Historic Data Analytics, developers will be able to load datasets for training and testing purposes to deploy a prediction model.
Once the developer considers the analytics model is finished, it can be published in the vf-Store for its usage within the Process Designer.
Regarding the Streaming Data Analytics, developers will use a REST API to define stream analysis modules. In each module, several conditions
can be established, involving mathematical operators, streaming data and user defined thresholds. When a condition is met, an alarm is raised
by the Streaming Data Analytics.
</p>
<p>For developing and using these analytics libraries, the developers need to have access and re-use the following vf-OS elements:</p>
<ul class="fa-ul">
<li><i class="fa-li fa fa-square"></i>vf-OS Studio</span> </li>
<li><i class="fa-li fa fa-square"></i>vf-Store</span> </li>
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<h4 style="margin-bottom:10px;">vf-OS Studio</h4>
<p>
The development of any vApp starts with the access to the specific development IDE that vf-OS provides. Inside the Studio,
a direct access to the Data Analytics is provided to allow the developer to develop the analytics libraries. Once these are
ready, the Data Analytics allows to deploy them as standalone libraries.
</p>
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<h4 style="margin-bottom:10px;">vf-Store</h4>
<p>
These deployed libraries are then annotated, uploaded and published to the vf-Store so that they are accessible
for being part of a vApp.
</p>
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<h6>About vf-OS</h6>
<p>
vf-OS offers a manufacturing orientated cloud platform, supporting a multi-sided market
ecosystem that provides a range of services for the connected factory of the future,
allowing manufacturing companies to develop and integrate better manufacturing and
logistics processes
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