Transwarp?StellarDB is a self-developed distributed graph database, compatible with openCypher query language, provides massive graph data storage and analysis capabilities, supports native graph storage structure, supports trillion-edge PB-level data storage. At the same time, StellarDB has millisecond level point edge query capability, 10+ layers of deep link analysis capability, provides nearly 40 kinds of graph analysis algorithms, with data 2D and 3D display capability. Starring StellarDB is applied in finance, government and social networks, and has realized the storage and stable operation of trillion-edge scale in a certain telecommunication relational mapping scenario, which really puts the application of trillion-grade graph database capability on the ground.
Typical application scenarios of graph database:
Knowledge graph:
In terms of graph database, knowledge graph is the application scenario that is most closely related to graph database with the widest application scope. Knowledge graphs intelligently process massive amounts of information to form large-scale knowledge bases and then support business applications.
The graph database in the knowledge graph has technical advantages in both storage and query: storage: the graph database provides flexible design patterns; query: the graph database provides efficient association query
As the underlying application of the graph database, the knowledge graph can provide services for a variety of industries, and the specific application scenarios, such as e-commerce, finance, law, medical care, smart home and so on. Decision-making system, recommendation system, intelligent Q&A, etc. in many fields.
Risk Compliance Knowledge Graph: Risk is the lifeblood of finance and the backbone of national regulatory technology. The knowledge graph of financial regulation + risk compliance is the earliest direction in which Starring Technology started to invest in construction and technology development. For the ultra-large-scale graph network, Starring Technology took the lead in releasing a graph display that supports spatial 3D, avoiding the drawbacks that the display of two-dimensional graphs cannot be clearly reflected for graphs with more than 10,000 nodes; at the same time, combining with the anti-money-laundering network mapping to utilize the attribute graph in which the nodes have geo-location attributes, it constructed a network of cross-border suspicious fund transfer graphs, which is easy to see the suspicious cross-border transactions at a glance.
Precision Marketing Knowledge Graph: Large financial institutions may have tens of millions of B-end or C-end users, how to realize precision marketing for different users? In terms of marketing knowledge graph, Starring Technology has developed the technology of public knowledge graph for banks, which realizes the precipitation of business knowledge in the marketing end, gives full play to the value of the graph, and helps banks to realize applications such as the precise placement of credit for small and micro-enterprises during epidemics.
Investment research class support mapping: In the financial and capital markets, the most important financial business is investment, the use of knowledge graph to portray human research results, knowledge mapping expression and construction, but also a number of brokerage firms and fund companies in the exploration of fintech empowered by the effect of the development of the roadmap for the investment income. In terms of investment knowledge graph, Starring Ring Technology, through the full-stack capabilities, deep integration of NLP + knowledge graph technology, through knowledge representation learning and other leading knowledge graph technology, to realize intelligent investment research knowledge graph, empowering investment research scene application.
Financial field
In the financial field, the graph database can y portray transaction behaviors by using multi-dimensional cross-correlation information, which can effectively identify large-scale and hidden fraud networks, and combined with relevant algorithms, such as machine learning, cluster analysis, and risk propagation, it can compute the user's risk scores in real time, and identify the risky behaviors in advance, effectively helping financial institutions to improve their investment research scenarios. It can effectively help financial institutions improve efficiency and reduce risk by pre-identifying risky behaviors before they occur.
Anti-fraud: Through the correlation of key entity information such as account, transaction, phone, IP address, geographic location, and other key entities, the N-layer graph mining of risk-exposed people helps to screen suspected fraudulent people for prevention purposes.
Anti-fraud Credit Guarantee Circle: Small and medium-sized enterprises (SMEs) guarantee each other through affiliates, customers upstream and downstream of the industrial chain, and relatives, forming a "guarantee network" with complex relationships, and the mining of the credit guarantee circle is of great significance in the identification and prevention of enterprise loan risks.
Equity penetration: Usually a complex network of executives, enterprises and affiliates, with equity as the link, penetrating upward to the ultimate actual controller of the target enterprise, and downward to all enterprises and their shareholders at any level of equity investment in the enterprise.
More application scenarios for graph databases
Financial sector?
Government and Enterprise : Internet of Things, Smart City, Road Planning, Intelligent Transportation, Trajectory Analysis, Epidemic Prevention and Control, Sending Relationship Imaging
Government and Enterprise : Internet of Things, Smart City, Road Planning, Intelligent Transportation, Trajectory Analysis, Epidemic Prevention and Control, Sending Relationship Imaging, etc.
Telecommunications: in-depth business analysis, anti-harassment, telecom fraud prevention, carrier business analysis, etc.Retail? : Intelligent recommendation, precise marketing, supply chain management, goods recommendation, browsing track analysis, etc.
Social domain? : community discovery, friend recommendation, interest user recommendation, opinion tracking, etc.
Industrial domain? : power grid analysis, supply chain management, equipment management, logistics analysis, etc.
Healthcare? : intelligent diagnostics, electronic medical records, healthcare & insurance analytics, etc.