Discover the power of NebulaGraph's graph database in delivering lightning-fast, personalized recommendations using real-time data analysis. Harness the potential of graph database technology to offer your users tailored articles, videos, products, and service suggestions.
Leverage graph database capabilities to overcome real-time recommendation challenges:
Traverse intricate, highly correlated data in real-time with the NebulaGraph database
Guarantee optimal real-time traversal query performance
Manage the rapid growth of business data volumes
NebulaGraph's enterprise-grade graph database solution provides native graph storage, ensuring the efficient and real-time traversal of highly correlated complex data.
Additionally, its shared-nothing architecture guarantees system scalability, making NebulaGraph the go-to choice for graph database-driven real-time recommendations.
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 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.
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.