Project documentation has been updated.

.gitignore

@@ -1,5 +1,4 @@
-*.class
-*.log
+.DS_Store
 target/
 
 # User-specific stuff:

README.md

@@ -6,19 +6,20 @@ The system layers:
 
  - Speed: based on the standard deviation
  - Batch: Random Forest classification (using [Smile](https://haifengl.github.io/smile/) engine)
- - Serving: Akka/Scala cluster with in-memory Redis database and persistance with Cassandra
+ - Serving: [Akka](https://akka.io/) cluster with in-memory Redis database and persistence with Cassandra
 
 The data flow:
 
- 1. MQTT messages are produced by the IoT emulator (`Producer` actor)
- 2. MQTT subscriber saves the messages into Cassandra database (`Consumer` actor)
- 3. The Random Forest model is constantly trained by the messages (`Trainer` actor)
- 4. HTTP endpoint requests the computation using the trained model and heuristics (`Analyzer` actor)
+ 1. MQTT messages are produced by the IoT emulator ([Producer](src/main/scala/mqtt/Producer.scala) actor)
+ 2. MQTT subscriber saves the messages into Cassandra database ([Consumer](src/main/scala/mqtt/Consumer.scala) actor)
+ 3. The Random Forest model is constantly trained by the messages ([Trainer](src/main/scala/analyzer/Trainer.scala) actor)
+ 4. HTTP endpoint requests the computation using the trained model and heuristics ([Analyzer](src/main/scala/analyzer/Analyzer.scala) actor)
 
 # Table of Contents
 
 * [A Sample of Lambda architecture project](#a-sample-of-lambda-architecture-project)
   * [Requirements](#requirements)
+    * [Cluster client requirements](#cluster-client-requirements)
   * [Usage](#usage)
     * [IoT emulation](#iot-emulation)
     * [Interactive processing](#interactive-processing)
@@ -43,6 +44,16 @@ Optionally you may install:
  - [Graphviz](http://www.graphviz.org/) visualization software (its dot utility is used for
  the Decision Tree visualization in the sample REPL session)
  - [Hey](https://github.com/rakyll/hey) HTTP load generator (used for the performance tests)
+ - [Scala](https://www.scala-lang.org/download/) shell (used for [the helper script](start.sc) for clustering)
+
+### Cluster client requirements
+
+The cluster clients only uses Scala shell and SBT. To verify the installations, please use:
+
+    $ sbt compile
+    $ scala start.sc client --dry-run
+
+It should create the target jar (`target/scala-2.12` directory) and configurations (`target/env` directory).
 
 ## Usage
 
@@ -62,10 +73,17 @@ Run the system (for the convenience, all microservices are packaged into the one
 
     $ sbt run
 
+Please note that Random Forest classification requires at least two classes in the input
+data (that means that the analyzed messages should contain anomalies). Please use the
+MQTT producer described below to generate anomalies,
+otherwise the [Trainer](src/main/scala/analyzer/Trainer.scala) shows the errors.
+
 ### IoT emulation
 
 Modify the sensor values with the Producer: http://localhost:8081
 
+![](resources/img/producer.png?raw=true)
+
 Verify the messages by subscribing to the required MQTT topic:
 
     $ mosquitto_sub -t sensors/power
@@ -74,6 +92,8 @@ Verify the messages by subscribing to the required MQTT topic:
 
 Verify the data stores with the Dashboard: http://localhost:8080
 
+![](resources/img/timeline.png?raw=true)
+
 Verify the entries data store using CQL:
 
     $ cqlsh -e "select * from sandbox.entry limit 10;"
@@ -239,4 +259,6 @@ To manually get the performance metrics please use the hey tool:
     $ hey -n 500 -c 10 -t 10 http://127.0.0.1:8082/
     $ hey -n 500 -c 10 -t 10 http://127.0.0.1:8082/stress
 
-Otherwise the stats is available via the Dashboard.
+Otherwise the stats is available via the Dashboard: http://localhost:8080
+
+![](resources/img/performance.png?raw=true)