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ako pushed a commit to branch new-web
in repository https://gitbox.apache.org/repos/asf/age-website.git
The following commit(s) were added to refs/heads/new-web by this push:
new dcd79b66 Update New web (#315)
dcd79b66 is described below
commit dcd79b669e08e4a06f95db6eb2fc0af7b5141933
Author: dpdjvhxm <[email protected]>
AuthorDate: Tue Apr 23 19:17:54 2024 +0900
Update New web (#315)
* Test
* Update gatsby-config.js
* Changes search result text
* Fix gatsby-config.js and title
* Change meta description
* Fix sitemap.xml for broken links
* Update landing page: Key Update and image
* Create Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update Blog
“2024-04-21-how-to-integrate-langchain-with-apache-age-for-graph-database-applications”
* Update blog, cms
* Update blog
* Fix spelling error
* Activate "blog"
* Update download page, modified blog
* modified footer, download
* Update landing image and blog scss
* modified: src/components/styles/Blog-post.scss
* modified: src/pages/download/index.md
* modified: src/templates/blog-post.js
* Create Blog
“2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models”
* Create Blog
“2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis”
* Create Blog
“2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1”
* Create Blog
“2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2”
* Create Blog “2024-04-23-graph-database-in-postgresql-apache-age”
* Create Blog “2024-04-23-comparison-of-apache-age-postgraphile-and-hasura”
* modified: static/admin/config.yml
* modified: src/components/styles/Blog-post.scss
modified: src/components/styles/BlogRollTemplate.module.scss
modified: src/templates/blog-post.js
* Update Blog “blog_240401”
* Match file name format
* deleted: src/pages/blog/blog_240401.md
* Change button color
* modified: static/sitemap.xml
* modified blog
---
src/components/styles/Blog-post.scss | 8 +-
src/components/styles/BlogRollTemplate.module.scss | 35 +
...01-easy-and-smooth-start-to-graph-databases.md} | 2 +-
...use-of-chatgpt-with-interactive-graph-models.md | 3 +-
...-cyber-attack-with-apache-ages-link-analysis.md | 3 +-
...arison-of-apache-age-postgraphile-and-hasura.md | 48 ++
...-hyperconnectivity-in-e-commerce-data-part-1.md | 62 ++
...4-23-graph-database-in-postgresql-apache-age.md | 21 +
...ing-with-graph-in-hyperconnected-data-part-2.md | 71 ++
src/templates/blog-post.js | 3 +
static/admin/config.yml | 2 +-
static/sitemap.xml | 720 +++++++++++++--------
12 files changed, 715 insertions(+), 263 deletions(-)
diff --git a/src/components/styles/Blog-post.scss
b/src/components/styles/Blog-post.scss
index 6a2e4df8..674ff79e 100644
--- a/src/components/styles/Blog-post.scss
+++ b/src/components/styles/Blog-post.scss
@@ -4,7 +4,7 @@
// 기본 스타일 설정
padding: 0.5em 1em; // 패딩
border: none; // 테두리 없음
- background-color: #4CAF50; // 배경색
+ background-color: #B8145A; // 배경색
color: white; // 텍스트 색상
text-align: center; // 텍스트 중앙 정렬
text-decoration: none; // 텍스트 밑줄 없음
@@ -15,7 +15,7 @@
// 마우스 오버 시 스타일 변경
&:hover {
- background-color: #45a049; // 호버 상태의 배경색
+ background-color: #B8145A; // 호버 상태의 배경색
}
// 포커스 시 스타일 변경
@@ -26,10 +26,10 @@
// 활성화(클릭) 상태 스타일
&:active {
- background-color: #3e8e41; // 클릭 상태의 배경색
+ background-color: #B8145A; // 클릭 상태의 배경색
}
}
.section {
- padding: 5% 10%; // 상하 패딩은 0, 좌우 패딩은 10%
+ padding: 1% 10%; // 상하 패딩은 0, 좌우 패딩은 10%
}
diff --git a/src/components/styles/BlogRollTemplate.module.scss
b/src/components/styles/BlogRollTemplate.module.scss
index c9e4f406..b4844888 100644
--- a/src/components/styles/BlogRollTemplate.module.scss
+++ b/src/components/styles/BlogRollTemplate.module.scss
@@ -17,3 +17,38 @@
}
}
+.articles {
+ margin-bottom: 8px;
+ padding-bottom: 8px;
+ border-bottom: 1px solid #ccc; // 구분선 색상과 두께 조정
+
+ &:last-child {
+ border-bottom: none; // 마지막 요소에는 구분선 없음
+ }
+ }
+
+
+.post-meta {
+ position: absolute; // 이미지를 절대 위치로 설정합니다.
+ left: 50%; // 부모 컨테이너의 가운데로 설정합니다.
+ top: 50%; // 부모 컨테이너의 가운데로 설정합니다.
+ transform: translate(-50%, -50%); // 정확히 중앙에 오도록 조정합니다.
+ z-index: 10; // 다른 요소들 위에 오도록 z-index 값을 설정합니다.
+
+ // 필요한 경우 width와 height도 고정할 수 있습니다.
+ width: 100px; // 고정된 너비를 원할 경우
+ height: 100px; // 고정된 높이를 원할 경우
+}
+
+.featured-thumbnail {
+ position: absolute; // 이미지를 절대 위치로 설정합니다.
+ left: 50%; // 부모 컨테이너의 가운데로 설정합니다.
+ top: 50%; // 부모 컨테이너의 가운데로 설정합니다.
+ transform: translate(-50%, -50%); // 정확히 중앙에 오도록 조정합니다.
+ z-index: 10; // 다른 요소들 위에 오도록 z-index 값을 설정합니다.
+
+ // 필요한 경우 width와 height도 고정할 수 있습니다.
+ width: 100px; // 고정된 너비를 원할 경우
+ height: 100px; // 고정된 높이를 원할 경우
+}
+
\ No newline at end of file
diff --git a/src/pages/blog/blog_240401.md
b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md
similarity index 97%
rename from src/pages/blog/blog_240401.md
rename to src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md
index 75bf970c..857c22a6 100644
--- a/src/pages/blog/blog_240401.md
+++ b/src/pages/blog/2024-04-01-easy-and-smooth-start-to-graph-databases.md
@@ -4,7 +4,7 @@ title: Easy and Smooth Start to Graph Databases
date: 2024-04-01T17:00:00Z
description:
featuredpost: true
-featuredimage: '/img/blogimg01.png'
+featuredimage:
tags:
- Apache AGE
- Graph Database
diff --git
a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
index a1fb7cf6..0da6be0d 100644
---
a/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
+++
b/src/pages/blog/2024-04-23-beyond-chatgpt-how-to-maximize-the-use-of-chatgpt-with-interactive-graph-models.md
@@ -6,7 +6,8 @@ date: 2024-04-23T01:18:22.687Z
description: "Beyond ChatGPT: How to Maximize the Use of ChatGPT with
Interactive Graph Models."
featuredpost: true
-featuredimage: /img/logo.png
+featuredimage:
+
---
<!--StartFragment-->
diff --git
a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
index b268fada..ced2d275 100644
---
a/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
+++
b/src/pages/blog/2024-04-23-combating-cyber-attack-with-apache-ages-link-analysis.md
@@ -6,7 +6,8 @@ description: |-
Combating cyber attack with Apache AGE's link
analysis.
featuredpost: true
-featuredimage: /img/logo.png
+featuredimage:
+
---
<!--StartFragment-->
diff --git
a/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md
b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md
new file mode 100644
index 00000000..71c3ac13
--- /dev/null
+++
b/src/pages/blog/2024-04-23-comparison-of-apache-age-postgraphile-and-hasura.md
@@ -0,0 +1,48 @@
+---
+templateKey: blog-post
+title: Comparison of Apache AGE, PostGraphile, and Hasura
+date: 2024-04-23T02:17:02.242Z
+description: Comparison of Apache AGE, PostGraphile, and Hasura
+featuredpost: true
+featuredimage:
+---
+<!--StartFragment-->
+
+### **◾ Apache AGE: Graph Database for PostgreSQL**
+
+Apache AGE extends PostgreSQL, transforming it into a graph database. This
enables handling complex data relationships and graph structures, supplemented
by the power of Cypher queries for graph-specific operations.
+
+
+
+- - -
+
+
+
+### **◾ PostGraphile: GraphQL API from PostgreSQL Schema**
+
+PostGraphile is a tool for building a GraphQL API on top of an existing
PostgreSQL schema.\
+It's designed for efficiency in web development, allowing real-time data
access and manipulation through GraphQL interface.
+
+
+
+- - -
+
+
+
+### **◾ Hasura: Real-Time GraphQL Engine for PostgreSQL**
+
+Hasura is a GraphQL server engine that dynamically generates real-time GraphQL
APIs from a PostgreSQL database.\
+It emphasizes performance and developer productivity, with an easy setup
process and tools for managing access and queries.
+
+
+
+- - -
+
+
+
+### **◾ Conclusion**
+
+Apache AGE is ideal for sophisticated graph data tasks within PostgreSQL.\
+PostGraphile and Hasura excel in quick GraphQL API deployment for example for
web and mobile, complementing AGE's rich graph features.
+
+<!--EndFragment-->
\ No newline at end of file
diff --git
a/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md
b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md
new file mode 100644
index 00000000..27d62eb5
--- /dev/null
+++
b/src/pages/blog/2024-04-23-from-data-to-connections-leveraging-hyperconnectivity-in-e-commerce-data-part-1.md
@@ -0,0 +1,62 @@
+---
+templateKey: blog-post
+title: "From Data to Connections: Leveraging Hyperconnectivity in E-commerce
+ Data (Part 1.)"
+date: 2024-04-23T02:11:47.653Z
+description: "From Data to Connections: Leveraging Hyperconnectivity in
E-commerce Data (Part 1.)"
+featuredpost: true
+featuredimage:
+---
+
+
+<!--StartFragment-->
+
+Hyper-connectivity implies that everything is connected — people, data, and
things — depending on the development of the internet, communication
technologies, and more. When individuals generate data from various sources,
such as technology or environmental sensors, the resultant connection networks
are referred to as the hyper-connected society. With this heightened level of
connectivity, there is significant potential to drive innovation and
transformation in all aspects of society [...]
+
+E-commerce is an example of where the data transform into connections among
people (customers & sellers), data, and things (products.) It implements the
commercial process of buying and selling over the Internet, moreover, oversees
the production and consumption process. In this article, we'll build the
concept of Hyper-connectivity by taking E-commerce sample data and let you see
the data in action!
+
+##### Data Modeling with Relational Database
+
+Below is a table around a hypothetical customer's purchases at shopping mall A
modeled in Relational Database, including products, stores, payment
information, and the embedding relationships between stores.
+
+
+
+Figure 1. RDB Modeling with Payment Sample Data Table
+
+To achieve hyper-connectivity among datasets, it is crucial to understand the
relationships between them. This is where the 'JOIN' method comes into play for
analyzing tables. An entity-relationship diagram (ERD) visualizes real-world
data and facilitates the connections between tables of varying natures. The
relational database not only enables to comprehend the flow of data between
tables, but also uncovers the relationships among tables.
+
+##### Data Modeling with Graph Database
+
+A graph database is a NoSQL database that stores and manages relationships in
the form of visualization using 'nodes' and 'edges'. These databases enable
hyperconnectivity by searching the relationships between data.
+
+
+
+##### Querying Graph Relationship
+
+A graph database provides a more efficient way to query data connected in a
hyperconnected system. For example, Apache AGE provides a hybrid of table and
graph data operations, allowing you to write intuitive queries such as Cypher
(a graph query) along with SQL. Let's see how to extract and retrieve it using
both Cypher and SQL.
+
+##### Querying the Single Relationships
+
+First, we can ask simple questions to find out what products customers buy and
what services they use.
+
+
+
+Figure 3. Relationship Querying for Graph Relationships in Apache AGE
+
+The above query is a graph query (Cypher) that traverses the graph to extract
the relationship in the “match (a: customer)-\[r]-(b: product)†clause and
what product the customer buys. The graph query extracts the graph
relationships and utilizes the hybrid query, “select\~†and “from\~â€
clauses available in Apache AGE to make the graph output easier to manipulate
for users unfamiliar with graph databases.
+
+##### Querying the Complex Relationships
+
+You can also use Apache AGE to query for complex queries. In addition to the
customer who bought the example product above, you can query complex
relationships such as which product they bought, what product category, which
bank they used, and more.
+
+
+
+Figure 4. Sophisticated Relationship Querying for Graph Relationships in
Apache AGE
+
+In the MATCH clause, you can search the path you want to extract. In the {"(a:
customer)-\[r1]-(b: bank {name: 'AGE bank'})"} clause, you can query what bank
they used; in the (a)-\[r3]-(d: card) clause, what card they used, and in the
(a)-\[r4]-(e: product)-\[r5]-(f:category_3) clause, what products they bought
and what product category they belong to. These allow us to explore complex
relationships and extract which customers have the same card, bank, and more.
+
+As seen above, graph database queries exhibit hyperconnectivity implementation
much more efficiently. Notably, Apache AGE stands out as a database with its
hybrid query capability, enabling many including non-experts in graph queries
and derive results using SQL. Being a PostgreSQL extension, Apache AGE offers
the flexibility to leverage extensions tailored to specific situations and
domains, enhancing its practicality and adaptability.
+
+Are you interested in learning more about Apache AGE? [Learn More
Now](http://agedb.io/From-Data-to-Connections-Leveraging-Hyperconnectivity-in-E-commerce-Data.jsp#).
+
+<!--EndFragment-->
\ No newline at end of file
diff --git
a/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md
b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md
new file mode 100644
index 00000000..ccf84043
--- /dev/null
+++ b/src/pages/blog/2024-04-23-graph-database-in-postgresql-apache-age.md
@@ -0,0 +1,21 @@
+---
+templateKey: blog-post
+title: "Graph Database in PostgreSQL: Apache AGE"
+date: 2024-04-23T02:15:59.564Z
+description: "Graph Database in PostgreSQL: Apache AGE"
+featuredpost: true
+featuredimage: /img/logo.png
+---
+<!--StartFragment-->
+
+Graph database have gained popularity in recent years due to their ability to
handle complex relationships between data. Unlike traditional relational
database, which store data in tables, graph database represent data as nodes,
edges, and properties. Nodes represent entities, edges represent the
relationships between those entities, and properties represent the attributes
of both.
+
+PostgreSQL, a popular relational database, can also function as a graph
database through the use of an extension called Apache AGE. With Apache AGE,
users can leverage the flexibility and scalability of graph database while
still utilizing PostgreSQL's advanced SQL querying capabilities and transaction
support.
+
+To use Apache AGE, users must first install it as an extension and then model
their data as nodes and edges. Apache AGE comes with its own set of SQL
extensions, similar to Cypher, that allows users to query their graph database.
However, users can also still use SQL to query their graph database if desired.
+
+For those new to graph database, Apache AGE comes with a tutorial to help them
get started. Additionally, you may use the #apache-age tag for questions on
Stack Overflow, join the project's Discord channel, or open an issue on GitHub,
the Apache AGE community is readily available to provide support and answer
questions.
+
+Overall, graph database offer a new way of thinking about how to store and
query complex relationships between data. With Apache AGE, users can easily
transform their PostgreSQL database into a graph database and take advantage of
the benefits that come with this type of database.
+
+<!--EndFragment-->
\ No newline at end of file
diff --git
a/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md
b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md
new file mode 100644
index 00000000..ea29c32f
--- /dev/null
+++
b/src/pages/blog/2024-04-23-learn-machine-learning-with-graph-in-hyperconnected-data-part-2.md
@@ -0,0 +1,71 @@
+---
+templateKey: blog-post
+title: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.)
+date: 2024-04-23T02:13:19.760Z
+description: Learn Machine Learning with Graph in Hyperconnected Data (Part 2.)
+featuredpost: true
+featuredimage:
+---
+<!--StartFragment-->
+
+Following our previous article on Hyperconnected data, this article will delve
deeper into the applications of graph-based machine learning specifically in
the realm of e-commerce data. We will explore the practical implementation of
unsupervised learning-based 'Graph Clustering' and supervised learning-based
'Graph node/Link Prediction' techniques, highlighting their significance in
storing and harnessing results within a graph database. By understanding these
powerful approaches, we ca [...]
+
+[Read part 1 Now - From Data to Connections: Leveraging Hyperconnectivity in
E-commerce
Data.](</blog/From-Data-to-Connections-Leveraging-Hyperconnectivity-in-E-commerce-Data-(Part
1.) /index.html>)
+
+Graph Projection and Clustering Analysis
+
+
+
+Figure 1. Graph DB Modeling with Payment Sample Data Table
+
+Once we understand the intricate product-purchase relationships within our
e-commerce data, the next step is to cluster customers based on their shared
properties. To accomplish this, we employ the 'Graph Projection' technique,
which constructs a unified network by projecting a graph along specific
relationships. By utilizing this approach, we can effectively visualize and
analyze distinct customer clusters, uncovering valuable insights that can drive
personalized marketing strategies an [...]
+
+
+
+Figure 2. Graph Database Modeling with Payment Sample Data Table
+
+The relationship between Customer A and Customer B, connected through the
purchase relationships of the same product, creates a view that facilitates a
seamless connection between preceding relationships. This view acts as an edge,
linking the edges of the graph projection.
+
+##### Querying the Single Relationships
+
+First, we can ask simple questions to find out what products customers buy and
what services they use.
+
+Essentially, we can construct a network comprising customers who have
purchased the same product. From this network, we can employ a graph clustering
algorithm to group customers based on structural similarities within the graph.
The graph clustering algorithm analyzes the graph's structural features
independently, allowing for the identification of distinctive characteristics
within each cluster. Below is an example of a single network configuration and
its characteristics when using th [...]
+
+
+
+Figure 3. Creating Homogeneous Network and Graph Cluster in Apache AGE
+
+##### Querying Features of Clusters
+
+Focusing on products emerges a single network, revealing distinctive
characteristics specific to each cluster. This network offers valuable insights
and provides personalized recommendations to assist customers in finding what
they truly need. Moreover, the configuration of this network unveils new
meta-information beyond traditional categorization. Uncovering patterns of
products purchased together suggests previously unseen characteristics of
products, allowing for a deeper understandi [...]
+
+
+
+Figure 4. Exploration of Cluster Graph Merging with Meta Info in Apache AGE
+
+##### Node/Link Prediction
+
+The results above demonstrate the meaningfulness of item purchases within the
identified clusters. For instance, in Cluster 2, the purchase of cleaning
supplies is appropriately grouped with the keyword 'Moving and Cleaning',
enabling targeted product recommendations to customers in this cluster.
Similarly, in Cluster 4, the purchases of furniture supplies are accurately
associated with the keyword 'Interior,' allowing for precise suggestions to
enhance customers interior design needs. T [...]
+
+Beyond the established product categories, grouping, giving meta-information,
and managing products relevant to the current era are also of the AGE's
capability. These can be achieved utilizing Node/Link Prediction techniques,
which enable the prediction and establishment of connections for items that
have not yet been categorized or linked. By leveraging this approach, we can
offer tailored recommendations that suggest customers with customer's genuine
needs.
+
+Node prediction operates at the individual node level within the graph,
allowing for accurate predictions regarding the characteristics of a given
node. This prediction is analyzed based on the relationships with irregular
neighbouring nodes.
+
+Link Prediction is a technique to predict the likelihood of a connection
between two individual nodes without existing connections. By successfully
establishing these previously unexplored relationships, the utility of
information within the network significantly enhances.
+
+In addition to traditional recommendation systems that rely on
similarity-based approaches, Link prediction, when combined with Node
prediction, facilitates recommendations based on similarities and discovers
valuable information about the characteristics of newly defined nodes. This
expanded data discovery opens up new avenues for exploration and analysis,
ultimately boosting confidence in the generated results.
+
+
+
+Figure 5. Example of Node Prediction (Source: CS224W Chapter 6 Message passing
and Node Classification)
+
+
+
+Figure 6. Example of Link Prediction in Apache AGE (Source: Missing Link
Prediction using Common Neighbour and Centrality based Parameterized Algorithm)
+
+This article explored the concept of graph hyper-connection, the connection of
table data from a relational database to a graph structure. By harnessing the
power of hyperconnectivity between relational and graph databases, data can be
managed flexibly, leading to the creation of new value through not only simple
queries but also advanced analytics.
+
+To efficiently model and manage different types of data, an enterprise DBMS
like AGEDB is an ideal solution. It enables the identification and management
of relationships between data in tables, facilitating the extension of
knowledge associations and enabling comprehensive analysis through graph-based
approaches.
+
+<!--EndFragment-->
\ No newline at end of file
diff --git a/src/templates/blog-post.js b/src/templates/blog-post.js
index b7b653ca..49f72375 100644
--- a/src/templates/blog-post.js
+++ b/src/templates/blog-post.js
@@ -7,6 +7,9 @@ import Layout from "../components/Layout";
import Content, { HTMLContent } from "../components/Content";
import style from "../components/styles/Blog-post.scss"
+// 블로그 글 게시하는 곳에 대한 내용
+
+
// eslint-disable-next-line
export const BlogPostTemplate = ({
content,
diff --git a/static/admin/config.yml b/static/admin/config.yml
index 3eb2dd0a..49b49d4e 100644
--- a/static/admin/config.yml
+++ b/static/admin/config.yml
@@ -199,6 +199,6 @@ collections:
- { label: "Publish Date", name: "date", widget: "datetime" }
- { label: "Description", name: "description", widget: "text" }
- { label: "Featured Post", name: "featuredpost", widget: "boolean" }
- - { label: "Featured Image", name: "featuredimage", widget: image }
+ - { label: "Featured Image", name: "featuredimage", widget: image,
required: "false" }
- { label: "Body", name: "body", widget: "markdown" }
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