Graph visualization intuitively and intelligently shows the structure and relationship between data
Can see what was previously invisible in a table or graph
——This article is excerpted from the "New Programmer" article
With the advent of the big data era, traditional relational databases have become increasingly difficult to meet a large number of frequently changing requirements due to their limitations in data modeling and storage. Relational databases, despite the word "relational" in their name, are not very good at handling the query and analysis of complex relationships. In addition, relational databases also lack the ability to scale horizontally on multiple servers. Based on this, a type of non-relational database, collectively referred to as "NoSQL" storage, came into being, and was widely researched and applied soon.
NoSQL (Not Only SQL, non-relational database) is a wide-ranging and diverse data persistence solution. They do not follow the relational database model and do not use SQL as a query language. Its data storage does not require a fixed table mode, and often avoids the use of SQL JOIN operations, and generally has the characteristics of horizontal scalability.
Comparison of graph database and relational database
Graph Database refers to a type of database that represents, stores and queries data in graphs. The "graph" here has nothing to do with pictures, graphs, charts, etc., but is based on the concept of "graph theory" in the field of mathematics, which is usually used to describe a certain relationship between certain things. For example in our daily life:
Social networks are graphs. The participants of each social network are nodes, and our interactions in the social network, such as "adding friends" and "likes" are the edges connecting the nodes.
City traffic is illustration. Each intersection, house number, bus stop, etc. is a node, and the street or bus line is an edge, connecting the places that can be reached.
Knowledge is also a graph. Each name, concept, person, event, etc. is a node, and the relationship between category, classification, causality, etc. is an edge, connecting the nodes to form a huge, rich and evolving knowledge graph.
"Graphs are everywhere", and because of this, traditional relational databases are not good at dealing with relational problems, which can be well solved by graph databases. Graph databases were born to solve this problem.
Knowledge Graph about Novel Coronavirus
The new crown virus is raging around the world. A group of members from the Neo4j graph database community integrated multiple heterogeneous biomedical and environmental datasets to build a knowledge graph about the new crown virus
https://github.com/covid-19-net/covid-19-community
To help researchers analyze the interaction between host, pathogen, environment and virus.
Knowledge Graph about Novel Coronavirus
Part of the visualization results of the knowledge graph, the leftmost part of the graph is the geographical submap of the virus outbreak, including countries, regions, and cities;
The part in green in the middle is the epidemiology submap, which includes information about virus strains, pathogens, and host organisms, with cases and strains associated with reports and locations where they were found, respectively;
The purple part on the right is the biological subgraph, representing organisms, genomes, chromosomes, variants, etc.
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The future of graph databases shines brightly
In the financial field, graphs and graph analysis help institutions find abnormal related party transactions more efficiently to win the war against money laundering.
In the electric power and telecommunications industries, graph databases help manage complex and huge equipment and line networks, and analyze root causes and estimate impacts for failures in a timely manner.
In the fields of manufacturing, scientific research, and medicine, graph databases are widely used to store and query knowledge graphs, and become an important support for big data management, data analysis, value mining, and even artificial intelligence technology.
In the foreseeable future, the combined application of graph database and artificial intelligence technology will bring more innovations and leaps. Graph databases can help improve AI capabilities in at least the following four areas.
Database technology development trends (as of June 2021) Source: DB-Engines
The first is the knowledge graph, which provides domain-related knowledge/context for decision support and helps ensure that the answer is appropriate for that particular situation.
Second, graphs provide higher processing efficiency, so using graphs to optimize models and accelerate the learning process can effectively enhance the efficiency of machine learning.
Third, feature extraction analysis based on data relationships can identify the most predictive elements in the data. Predictive models based on strong features found in the data have higher accuracy.
Fourth, the graph provides a method to ensure the transparency of AI decision-making, which makes the conclusions obtained through AI more interpretable. AI and machine learning have great application potential, and graphs unlock that potential. This is because graph database technology supports domain-related knowledge and linked data, making AI more widely applicable.
Author of database articles for The New Programmer
In recent years, cloud-deployed graph databases (SaaS/DaaS) have become another development trend. Many major domestic manufacturers have launched self-developed cloud graph database products, such as Baidu's HugeGraph, Ali's GDB, Tencent's TGDB, and Huawei's GES graph computing engine.
As far as the overall trend is concerned, we can foresee that in the era of big data, data loss is no longer the biggest challenge. What we are eager for is the ability to mine the value of data, and a large part of the value of data lies in the correlation between data. As the most effective technologies and methods for processing linked data, graph database and graph analysis will continue to shine and write a new chapter in database applications.
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