Github user hmcl commented on a diff in the pull request:
https://github.com/apache/storm/pull/1816#discussion_r92249343
--- Diff: external/storm-pmml/README.md ---
@@ -0,0 +1,104 @@
+#Storm PMML Bolt
+ Storm integration to load PMML models and compute predictive scores for
running tuples. The PMML model represents
+ the machine learning (predictive) model used to do prediction on raw
input data. The model is typically loaded into a
+ runtime environment, which will score the raw data that comes in the
tuples.
+
+#Create Instance of PMML Bolt
+ To create an instance of the `PMMLPredictorBolt` you must provide a
`ModelRunner` using a `ModelRunnerFactory`,
+ and optionally an instance of `ModelOutputFields`. The
`ModelOutputFields` is only required if you wish to emit
+ tuples with predicted scores to one or multiple streams. Otherwise, the
`PMMLPredictorBolt` will declare no
+ output fields.
+
+ The `ModelRunner` represents the runtime environment to execute the
predictive scoring. It has only one method:
+
+ ```java
+ Map<Stream, List<Object>> scoredTuplePerStream(Tuple input);
+ ```
+
+ This method contains the logic to compute the scored tuples from the raw
inputs tuple. It's up to the discretion of the
+ implementation to define which scored values are to be assigned to each
`Stream`. A `Stream` is a representation of a Storm stream.
+
+ The `PmmlModelRunner` is an extension of `ModelRunner` that represents
the typical steps involved
+ in predictive scoring. Hence, it allows for the **extraction** of raw
inputs from the tuple, **pre process** the
+ raw inputs, and **predict** the scores from the preprocessed data.
+
+ The `JPmmlModelRunner` is an implementation of `PmmlModelRunner` that
uses [JPMML](https://github.com/jpmml/jpmml) as
+ runtime environment. This implementation extracts the raw inputs from the
tuple for all `active fields`,
+ and builds a tuple with the predicted scores for the `predicted fields`
and `output fields`.
+ In this implementation all the declared streams will have the same scored
tuple.
+
+ The `predicted`, `active`, and `output` fields are extracted from the
PMML model.
+
+#Run Bundled Examples
+
+To run the examples you must copy the `storm-pmml` uber jar to
`STORM-HOME/extlib` and then run the command:
+
+ ```java
+ STORM-HOME/bin/storm jar
STORM-HOME/external/storm-pmml/storm-pmml-examples-2.0.0-SNAPSHOT.jar
+ org.apache.storm.pmml.JpmmlRunnerTestTopology jpmmlTopology PMMLModel.xml
RawInputData.csv
+ ```
+#Build Uber JAR
+
+To build the uber jar with all the dependencies for the module
`storm-pmml` you must run the command
+
+```
+mvn package -f REPO_HOME/external/storm-pmml/pom.xml
+```
+
+after adding the following declaration to
`REPO_HOME/storm/external/storm-pmml/pom.xml`
+
+```
+<build>
+ <plugins>
+ <plugin>
+ <groupId>org.apache.maven.plugins</groupId>
+ <artifactId>maven-shade-plugin</artifactId>
+ <configuration>
+
<createDependencyReducedPom>true</createDependencyReducedPom>
+ </configuration>
+ <executions>
+ <execution>
+ <phase>package</phase>
+ <goals>
+ <goal>shade</goal>
+ </goals>
+ <configuration>
+ <transformers>
+ <transformer
implementation="org.apache.maven.plugins.shade.resource.ServicesResourceTransformer"/>
+ <transformer
implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
+
<mainClass>org.apache.storm.pmml.JpmmlRunnerTestTopology</mainClass>
+ </transformer>
+ </transformers>
+ </configuration>
+ </execution>
+ </executions>
+ </plugin>
+ </plugins>
+ </build>
+```
+
+
+## License
+
+Licensed to the Apache Software Foundation (ASF) under one
+or more contributor license agreements. See the NOTICE file
+distributed with this work for additional information
+regarding copyright ownership. The ASF licenses this file
+to you under the Apache License, Version 2.0 (the
+"License"); you may not use this file except in compliance
+with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing,
+software distributed under the License is distributed on an
+"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, either express or implied. See the License for the
+specific language governing permissions and limitations
+under the License.
+
+
+## Committer Sponsors
+ * Sriharsha Chintalapani
([srihar...@apache.org](mailto:srihar...@apache.org))
+
+This general abstraction has the purpose of supporting arbitrary
implementations that compute predicted scores from raw inputs
--- End diff --
Good catch. I forgot to clean this up. This info is already mentioned in
the introduction section in slight different way, but with the same exact
meaning. Will delete it.
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