Merge pull request #516 from ashwinkumar12345/renaming_open_distro

Renaming to "Open Distro"

Author: ashwinkumar12345

docs/ad/index.md

@@ -11,7 +11,7 @@ An anomaly is any unusual change in behavior. Anomalies in your time-series data
 
 Discovering anomalies using conventional methods such as creating visualizations and dashboards can be challenging. You can set an alert based on a static threshold, but this requires prior domain knowledge and is not adaptive to data that exhibits organic growth or seasonal behavior.
 
-The anomaly detection feature automatically detects anomalies in your Elasticsearch data in near real-time using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream to compute an `anomaly grade` and `confidence score` value for each incoming data point. These values are used to differentiate an anomaly from normal variations. For more information about how RCF works, see [Random Cut Forests](https://pdfs.semanticscholar.org/8bba/52e9797f2e2cc9a823dbd12514d02f29c8b9.pdf?_ga=2.56302955.1913766445.1574109076-1059151610.1574109076).
+The anomaly detection feature automatically detects anomalies in your data in near real-time using the Random Cut Forest (RCF) algorithm. RCF is an unsupervised machine learning algorithm that models a sketch of your incoming data stream to compute an `anomaly grade` and `confidence score` value for each incoming data point. These values are used to differentiate an anomaly from normal variations. For more information about how RCF works, see [Random Cut Forests](https://pdfs.semanticscholar.org/8bba/52e9797f2e2cc9a823dbd12514d02f29c8b9.pdf?_ga=2.56302955.1913766445.1574109076-1059151610.1574109076).
 
 You can pair the anomaly detection plugin with the [alerting plugin](../alerting/) to notify you as soon as an anomaly is detected.
 

docs/alerting/api.md

@@ -247,7 +247,7 @@ For a full list of timezone names, refer to [Wikipedia](https://en.wikipedia.org
 
 ## Update monitor
 
-When you update a monitor, include the current version number as a parameter. Open Distro for Elasticsearch increments the version number automatically (see the sample response).
+When you update a monitor, include the current version number as a parameter. Open Distro increments the version number automatically (see the sample response).
 
 #### Request
 

docs/alerting/settings.md

@@ -30,7 +30,7 @@ GET _cat/indices?expand_wildcards=open,hidden
 
 We don't recommend changing these settings; the defaults should work well for most use cases.
 
-All settings are available using the Elasticsearch `_cluster/settings` API. None require a restart, and all can be marked `persistent` or `transient`.
+All settings are available using the `_cluster/settings` API. None require a restart, and all can be marked `persistent` or `transient`.
 
 Setting | Default | Description
 :--- | :--- | :---

docs/im/ism/index.md

@@ -57,7 +57,7 @@ PUT _opendistro/_ism/policies/policy_id
 
 For an example ISM template policy, see [Sample policy with ISM template](policies/#sample-policy-with-ism-template).
 
-For ODFE versions prior to 1.13.0, include the `policy_id` in an index template so when an index is created that matches the index template pattern, the index will have the policy attached to it:
+For Open Distro versions prior to 1.13.0, include the `policy_id` in an index template so when an index is created that matches the index template pattern, the index will have the policy attached to it:
 
 ```json
 PUT _index_template/<template_name>

docs/im/ism/settings.md

@@ -13,7 +13,7 @@ We don't recommend changing these settings; the defaults should work well for mo
 
 Index State Management (ISM) stores its configuration in the `.opendistro-ism-config` index. Don't modify this index without using the [ISM API operations](../api/).
 
-All settings are available using the Elasticsearch `_cluster/settings` operation. None require a restart, and all can be marked `persistent` or `transient`.
+All settings are available using the `_cluster/settings` operation. None require a restart, and all can be marked `persistent` or `transient`.
 
 Setting | Default | Description
 :--- | :--- | :---

docs/images/ta.svg

@@ -7,4 +7,5 @@ has_children: false
 ---
 
 # JNI Library
+
 In order to integrate [nmslib's](https://github.com/nmslib/nmslib/) approximate k-NN functionality, which is implemented in C++, into the k-NN plugin, which is implemented in Java, we created a Java Native Interface library. Check out [this wiki](https://en.wikipedia.org/wiki/Java_Native_Interface) to learn more about JNI. This library allows the k-NN plugin to leverage nmslib's functionality. For more information about how we build the JNI library binary and how to get the most of it in your production environment, see [here](https://github.com/opendistro-for-elasticsearch/k-NN#jni-library-artifacts).

docs/knn/jni-library.md

@@ -8,6 +8,7 @@ has_math: true
 ---
 
 # Exact k-NN with Scoring Script
+
 The k-NN plugin implements the Elasticsearch score script plugin that you can use to find the exact k-nearest neighbors to a given query point. Using the k-NN score script, you can apply a filter on an index before executing the nearest neighbor search. This is useful for dynamic search cases where the index body may vary based on other conditions. Because this approach executes a brute force search, it does not scale as well as the [Approximate approach](../approximate-knn). In some cases, it may be better to think about refactoring your workflow or index structure to use the Approximate approach instead of this approach.
 
 ## Getting started with the score script for vectors
@@ -96,7 +97,7 @@ All parameters are required.
 - `space_type` corresponds to the distance function. See the [spaces section](#spaces).
 
 
-*Note* -- After ODFE 1.11, `vector` was replaced by `query_value` due to the addition of the `bithamming` space.
+*Note* -- After Open Distro 1.11, `vector` was replaced by `query_value` due to the addition of the `bithamming` space.
 
 
 The [post filter example in the approximate approach](../approximate-knn/#using-approximate-k-nn-with-filters) shows a search that returns fewer than `k` results. If you want to avoid this situation, the score script method lets you essentially invert the order of events. In other words, you can filter down the set of documents you want to execute the k-nearest neighbor search over.
@@ -171,7 +172,7 @@ GET my-knn-index-2/_search
 ```
 
 ## Getting started with the score script for binary data
-The k-NN score script also allows you to run k-NN search on your binary data with the Hamming distance space. 
+The k-NN score script also allows you to run k-NN search on your binary data with the Hamming distance space.
 In order to use Hamming distance, the field of interest must have either a `binary` or `long` field type. If you're using `binary` type, the data must be a base64-encoded string.
 
 This example shows how to use the Hamming distance space with a `binary` field type:

docs/knn/knn-score-script.md

@@ -12,7 +12,7 @@ Besides simple SFW (`SELECT-FROM-WHERE`) queries, the SQL plugin supports comple
 
 ## Joins
 
-Open Distro for Elasticsearch SQL supports inner joins, cross joins, and left outer joins.
+Open Distro SQL supports inner joins, cross joins, and left outer joins.
 
 ### Constraints
 

docs/sql/complex.md

@@ -9,7 +9,7 @@ nav_order: 73
 
 The following table shows the data types supported by the SQL plugin and how each one maps to SQL and Elasticsearch data types:
 
-| ODFE SQL Type | Elasticsearch Type | SQL Type
+| Open Distro SQL Type | Elasticsearch Type | SQL Type
 :--- | :--- | :---
 boolean |	boolean |	BOOLEAN
 byte |	byte |	TINYINT