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Use Cases

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 Real-time recommendation

Real-time recommendation

NebulaGraph offers the ability to instantly process the real-time information produced by a visitor and make accurate recommendations on articles, videos, products, and services.

By adding recommendation-related factors and data sources, companies can provide users with highly personalized real-time recommendations.

To realize highly personalized real-time recommendations, there are several challenges:
1. Traverse highly correlated complex data in real-time
2. Ensure real-time traversal query performance
3. The volume of business data increases rapidly

NebulaGraph provides native graph storage to ensure efficient and real-time traversal of highly correlated complex data, and its shared-nothing architecture ensures the scalability of the system.

Financial risk control

Financial risk control

Risk control is very important to the financial industry. Financial fraud poses a serious threat to the healthy development of the financial industry.

Financial institutions have to traverse countless transactions to piece together potential crimes and understand how combinations of transactions and devices might be related to a single fraud scheme.

The data involved in fraudulent transactions have a highly complex correlation, and the analysis results must be returned within the transaction time.

With the help of NebulaGraph, fraud rings and other sophisticated scams can be easily detected, and NebulaGraph provides high availability to ensure the continuous development of key business lines

Knowledge graph

Knowledge graph

Knowledge graph is widely used in chat robot, big data risk control, securities investment, intelligent medical treatment, adaptive education, recommendation system and other scenarios, and covers many fields such as pan Internet, finance, government affairs, medical treatment and so on.

To use property graph to express multiple business scenarios, the following challenges are faced:
1. The types of knowledge are diverse, and there are complex and intertwined relationships between entities and in reality
2. The storage problem of massive data

NebulaGraph uses property graph to store the relationship between entities, and It applies the separation of storage and computing architecture to make scaling easier.
By adding recommendation-related factors and data sources, companies can provide users with highly personalized real-time recommendations.

To realize highly personalized real-time recommendation, there are several challenges:
1. Traverse highly correlated complex data in real time
2. Ensure real-time traversal query performance
3. The volume of business data increases rapidly

NebulaGraph provides native graph storage to ensure efficient and real-time traversal of highly correlated complex data, and its shared-nothing architecture ensures the scalability of the system.

Network security

Network security

Sensitive information protection and potential threat detection are the most important for database of enterprises.

To track network and device paths and find potential attack threats, the following challenges are faced:
1. The attack path has complex and hidden relationship, which is difficult to be effectively identified by traditional defense means
2. Analyze whether the data or infrastructure is damaged, and screen massive data
3. An organized network attack can form an information island. It is necessary to quickly judge whether a single transaction is a part of the attack

NebulaGraph uses native graph storage and shared-nothing architecture to track the network and device paths to find potential threats and respond to network attacks ASAP.

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