diff --git a/README.md b/README.md
index 29e3d72..991d920 100644
--- a/README.md
+++ b/README.md
@@ -23,37 +23,18 @@ we can speed up the extraction of actionable infrastructure insight.
 ### Advantages
 :thumbsup: Decouple algorithm design from execution.
 
-:thumbsup: Reusable specification of the desired
-[flow](doc/design.md#flow).
+:thumbsup: Reusable specification of the desired [flow](doc/design.md#flow).
 
 :thumbsup: Language independent [flow](doc/design.md#flow) definition.
 
 :thumbsup: Data source and format independent.
 
-:thumbsup: Easy to add new [SML](doc/design.md#sml) algorithms and #
-combine them with pre-existing ones in the [flow](doc/design.md#flow).
+:thumbsup: Easy to add new [SML](doc/design.md#sml) algorithms and # combine them with pre-existing ones in the [flow](doc/design.md#flow).
 
 :thumbsup: Transparently exploit data parallelism.
 
 ### Example Use Cases
-Below are few use cases that are relevant to OpenStack. However, MoNanas
-enables you to add your own [data ingestors](doc/dev_guide.md#ingestors).
-
-| Example                       | Alert Fatigue Management | Anomaly Detection |
-|:------------------------------|:-------------------------|:------------------|
-| **Dataset**                   | Synthetic, but representative, set of Monasca alerts that are processed in a stream manner. This alert set represents alerts that are seen in a data center consisting of several racks, enclosures and nodes. | `iptables` rules together with the number of times they are fired in a time period. |
-| **Parsing**                   | Monasca alert parser. | Simple parser extracting period and number of fire events per rule. |
-| **SML algorithm flow**        | `filter(bad_formatted) -> filter(duplicates) -> aggregate() >> aggregator` aggregation can utilize conditional independence causality, score-based causality, linear algebra causality. | `detect_anomaly() >> aggregator` anomaly detection could be based on SVM, trend, etc. |
-| **Output**                    | Directed acyclic alert graph with potential root causes at the top. | Rule set with an anomalous number of firing times in a time period. |
-| **:information_source: Note** | Even though this could be consumed directly by devops, the usage of  [Vitrage MoNanas Sink](doc/getting_started.md#vitrage_sink) is recommended. The output of this module can speed up creation of a [Vitrage](https://wiki.openstack.org/wiki/Vitrage) entity graph to do further analysis on it. | None. |
-
-`->` indicates a sequential operation in the flow.
-
-`//` indicates beginning of group of operations running in parallel.
-
-`-` indicates operations running in parallel.
-
-`>>` indicates end of group of operations running in parallel.
+* [MoNanas/UseCases](doc/use_cases.md): Use cases that are relevant to OpenStack
 
 ### Documentation
 * [MoNanas/GettingStarted](doc/getting_started.md): A starting point for users
diff --git a/doc/use_cases.md b/doc/use_cases.md
new file mode 100644
index 0000000..6134a25
--- /dev/null
+++ b/doc/use_cases.md
@@ -0,0 +1,20 @@
+# Example Use Cases
+
+Below are few use cases that are relevant to OpenStack. However, MoNanas
+enables you to add your own [data ingestors](doc/dev_guide.md#ingestors).
+
+| Example                       | Alert Fatigue Management | Anomaly Detection |
+|:------------------------------|:-------------------------|:------------------|
+| **Dataset**                   | Synthetic, but representative, set of Monasca alerts that are processed in a stream manner. This alert set represents alerts that are seen in a data center consisting of several racks, enclosures and nodes. | `iptables` rules together with the number of times they are fired in a time period. |
+| **Parsing**                   | Monasca alert parser. | Simple parser extracting period and number of fire events per rule. |
+| **SML algorithm flow**        | `filter(bad_formatted) -> filter(duplicates) -> aggregate() >> aggregator` aggregation can utilize conditional independence causality, score-based causality, linear algebra causality. | `detect_anomaly() >> aggregator` anomaly detection could be based on SVM, trend, etc. |
+| **Output**                    | Directed acyclic alert graph with potential root causes at the top. | Rule set with an anomalous number of firing times in a time period. |
+| **:information_source: Note** | Even though this could be consumed directly by devops, the usage of  [Vitrage MoNanas Sink](doc/getting_started.md#vitrage_sink) is recommended. The output of this module can speed up creation of a [Vitrage](https://wiki.openstack.org/wiki/Vitrage) entity graph to do further analysis on it. | None. |
+
+`->` indicates a sequential operation in the flow.
+
+`//` indicates beginning of group of operations running in parallel.
+
+`-` indicates operations running in parallel.
+
+`>>` indicates end of group of operations running in parallel.