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id="page-title"><h1>Glossary</h1></div></div><div class="content"> 
<p><strong>Data Preparator</strong> - Part of Engine. It reads data from source 
and transforms it to the desired format.</p><p><strong>Data Source</strong> - 
Part of Engine. It preprocesses the data and forward it to the algorithm for 
model training.</p><p><strong>Engine</strong> - An Engine represents a type of 
prediction, e.g. product recommendation. It is comprised of four components: 
[D] Data Source and Data Preparator, [A] Algorithm, [S] Serving, [E] Evaluation 
Metrics.</p><p><strong>EngineClient</strong> - Part of PredictionSDK. It sends 
queries to a deployed engine instance
  through the Engine API and retrives prediction results.</p><p><strong>Event 
API</strong> - Please see Event Server.</p><p><strong>Event Server</strong> - 
Event Server is designed to collect data into PredictionIO in an event-based 
style. Once the Event Server is launched, your application can send data to it 
through its Event API with HTTP requests or with the EventClient of 
PredictionIO&#39;s SDKs.</p><p><strong>EventClient</strong> - Please see Event 
Server.</p><p><strong>Live Evaluation</strong> - Evaluation of prediction 
results in a production environment. Prediction results are shown to real 
users. Users do not rate the results explicitly but the system observes user 
behaviors such as click through rate.</p><p><strong>Offline Evaluation</strong> 
- The prediction results are compared with pre-compiled offline datasets. 
Typically, offline evaluations are meant to identify the most promising 
approaches.</p><p><strong>Test Data</strong> - Also commonly referred as Test 
Set. A set
  of data used to assess the strength and utility of a predictive 
relationship.</p><p><strong>Training Data</strong> - Also commonly referred as 
Training Set. A set of data used to discover potentially predictive 
relationships. In PredictionIO Engine, training data is processed through the 
Data layer and passed onto algorithm.</p></div></div></div></div><footer><div 
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<p><strong>Data Preparator</strong> - Part of Engine. It reads data from source 
and transforms it to the desired format.</p><p><strong>Data Source</strong> - 
Part of Engine. It preprocesses the data and forward it to the algorithm for 
model training.</p><p><strong>Engine</strong> - An Engine represents a type of 
prediction, e.g. product recommendation. It is comprised of four components: 
[D] Data Source and Data Preparator, [A] Algorithm, [S] Serving, [E] Evaluation 
Metrics.</p><p><strong>EngineClient</strong> - Part of PredictionSDK. It sends 
queries to a deployed engine instance
  through the Engine API and retrieves prediction results.</p><p><strong>Event 
API</strong> - Please see Event Server.</p><p><strong>Event Server</strong> - 
Event Server is designed to collect data into PredictionIO in an event-based 
style. Once the Event Server is launched, your application can send data to it 
through its Event API with HTTP requests or with the EventClient of 
PredictionIO&#39;s SDKs.</p><p><strong>EventClient</strong> - Please see Event 
Server.</p><p><strong>Live Evaluation</strong> - Evaluation of prediction 
results in a production environment. Prediction results are shown to real 
users. Users do not rate the results explicitly but the system observes user 
behaviors such as click through rate.</p><p><strong>Offline Evaluation</strong> 
- The prediction results are compared with pre-compiled offline datasets. 
Typically, offline evaluations are meant to identify the most promising 
approaches.</p><p><strong>Test Data</strong> - Also commonly referred as Test 
Set. A se
 t of data used to assess the strength and utility of a predictive 
relationship.</p><p><strong>Training Data</strong> - Also commonly referred as 
Training Set. A set of data used to discover potentially predictive 
relationships. In PredictionIO Engine, training data is processed through the 
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diff --git a/resources/intellij/index.html b/resources/intellij/index.html
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+++ b/resources/intellij/index.html
@@ -4,7 +4,7 @@
 </pre></td></tr></tbody></table> </div></li> <li><p> Program 
arguments:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>--engine-id dummy 
--engine-version dummy --engine-variant engine.json --env <span 
class="nv">dummy</span><span class="o">=</span>dummy
 </pre></td></tr></tbody></table> </div></li> </ul> <p>Make sure <em>Working 
directory</em> is set to the base directory of the template that you are 
working on.</p><p>Click the folder button to the right of <em>Environment 
variables</em>, and paste the relevant values from <code>conf/pio-env.sh</code> 
in your PredictionIO installation directory. The following shows an example 
using JDBC and PostgreSQL.</p><p><img alt="Example environment variables 
settings" src="/images/intellij/pio-train-env-vars-36b4c5bd.png"/></p><p>Make 
sure <em>Include dependencies with &quot;Provided&quot; scope</em> is 
checked.</p><p>The end result should look something similar to this.</p><p><img 
alt="Run Configuration" 
src="/images/intellij/pio-train-e4117867.png"/></p><p>Save and you can run or 
debug <code>pio train</code> with the new configuration!</p><h3 
id='simulating-<code>pio-deploy</code>' class='header-anchors' >Simulating 
<code>pio deploy</code></h3><p>For <code>pio deploy</code>, simply duplicate
  the previous configuration and replace with the following.</p> <ul> <li><p> 
Main class:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td 
class="code"><pre>org.apache.predictionio.workflow.CreateServer
 </pre></td></tr></tbody></table> </div></li> <li><p> Program 
Arguments:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>--engineInstanceId 
&lt;id_from_pio_train&gt; --engine-variant engine.json
-</pre></td></tr></tbody></table> </div></li> </ul> <h3 id='executing-a-query' 
class='header-anchors'>Executing a Query</h3><p>You can execute a query with 
the correct SDK. For a recommender that has been trained with the sample 
MovieLens dataset perhaps the easiest query is a <code>curl</code> one. Start 
by running or debuging your <code>pio deploy</code> config so the service is 
waiting for the query. Then go to the &quot;Terminal&quot; tab at the very 
bottom of the IntelliJ IDEA window and enter the <code>curl</code> 
request:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>curl -H <span 
class="s2">"Content-Type: application/json"</span> -d <span class="s1">'{ 
"user": "1", "num": 4 }'</span> http://localhost:8000/queries.json
+</pre></td></tr></tbody></table> </div></li> </ul> <h3 id='executing-a-query' 
class='header-anchors'>Executing a Query</h3><p>You can execute a query with 
the correct SDK. For a recommender that has been trained with the sample 
MovieLens dataset perhaps the easiest query is a <code>curl</code> one. Start 
by running or debugging your <code>pio deploy</code> config so the service is 
waiting for the query. Then go to the &quot;Terminal&quot; tab at the very 
bottom of the IntelliJ IDEA window and enter the <code>curl</code> 
request:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre>curl -H <span 
class="s2">"Content-Type: application/json"</span> -d <span class="s1">'{ 
"user": "1", "num": 4 }'</span> http://localhost:8000/queries.json
 </pre></td></tr></tbody></table> </div> <p>This should return something 
like:</p><div class="highlight shell"><table style="border-spacing: 
0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1
 2
 3

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