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The following commit(s) were added to refs/heads/master by this push:
new 26a2e8d4 improve doc
26a2e8d4 is described below
commit 26a2e8d48fdd6a0b40a6d6316aff981ab1c5c397
Author: coderzc <[email protected]>
AuthorDate: Sun Nov 27 21:44:37 2022 +0800
improve doc
---
README.md | 2 +-
content/en/_index.html | 4 ++--
content/en/docs/introduction/README.md | 2 +-
3 files changed, 4 insertions(+), 4 deletions(-)
diff --git a/README.md b/README.md
index 2d4481e0..0c1e87c3 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ Please visit the [contribution doc](./contribution.md) to get
start, include the
HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system(Graph Database, [GitHub project
address](https://github.com/hugegraph/hugegraph)),
implemented the [Apache TinkerPop3](https://tinkerpop.apache.org) framework
and is fully compatible with the
[Gremlin](https://tinkerpop.apache.org/gremlin.html) query language,
With complete toolchain components, it helps users to easily build
applications and products based on graph databases. HugeGraph supports fast
import of more than 10 billion vertices and edges, and provides
millisecond-level relational query capability (OLTP).
-It also support large-scale distributed graph processing (OLAP).
+It supports large-scale distributed graph processing (OLAP).
Typical application scenarios of HugeGraph include deep relationship
exploration, association analysis, path search, feature extraction, data
clustering, community detection, knowledge graph, etc., and are applicable to
business fields such as network security, telecommunication fraud, financial
risk control, advertising recommendation, social network and intelligence
Robots etc.
diff --git a/content/en/_index.html b/content/en/_index.html
index b9b343ba..221a6627 100644
--- a/content/en/_index.html
+++ b/content/en/_index.html
@@ -28,8 +28,8 @@ linkTitle = "Huge Docs"
{{% blocks/lead color="primary" %}}
<p>HugeGraph supports fast import performance in the case of more than 10
billion Vertices and Edges</p>
-<p> Graph,millisecond-level OLTP query capability, and support large-scale
distributed</p>
-<p> graph processing for OLAP analysis. The main scenarios of HugeGraph
include</p>
+<p> Graph,millisecond-level OLTP query capability, and large-scale
distributed</p>
+<p> graph processing (OLAP). The main scenarios of HugeGraph include</p>
<p> correlation search, fraud detection, and knowledge graph.</p>
{{% /blocks/lead %}}
diff --git a/content/en/docs/introduction/README.md
b/content/en/docs/introduction/README.md
index b490334b..fd0c1e93 100644
--- a/content/en/docs/introduction/README.md
+++ b/content/en/docs/introduction/README.md
@@ -9,7 +9,7 @@ weight: 1
HugeGraph is an easy-to-use, efficient, general-purpose open source graph
database system(Graph Database, [GitHub project
address](https://github.com/hugegraph/hugegraph)),
implemented the [Apache TinkerPop3](https://tinkerpop.apache.org) framework
and is fully compatible with the
[Gremlin](https://tinkerpop.apache.org/gremlin.html) query language,
With complete toolchain components, it helps users to easily build
applications and products based on graph databases. HugeGraph supports fast
import of more than 10 billion vertices and edges, and provides
millisecond-level relational query capability (OLTP).
-It support large-scale distributed graph computing (OLAP).
+It supports large-scale distributed graph computing (OLAP).
Typical application scenarios of HugeGraph include deep relationship
exploration, association analysis, path search, feature extraction, data
clustering, community detection, knowledge graph, etc., and are applicable to
business fields such as network security, telecommunication fraud, financial
risk control, advertising recommendation, social network and intelligence
Robots etc.
