Our team has a deep understanding of designing, implementing, integrating, and optimizing the Neo4j graph database into your IT environment and existing application stack.
We assist in all stages of software development projects that employ or are considering using Neo4j. Whether you're just getting started and want to validate your graph model and software architecture, learn about graph database best practices, solve a real-world technical issue, boost performance, get some help transitioning into production, or obtain advice on long-term Neo4j database administration - GraphStars will be with you every step of the way.
Neo4j is a powerful graph database that can be used for fraud detection. It allows you to build a model of your data and then search for patterns and relationships that may indicate fraud.
Neo4j services for different types of business verticals.
Graph databases, unlike relational ones, can handle data analysis more naturally. The goal of graph database analysis is to reveal connections - the most important of which are often the trust and reputation relationships between entities. To do this, Neo4j offers several powerful features, including property graphs, pattern matching, and the Cypher query language. These make it possible to quickly find relationships between entities, as well as insights into how trust and reputation work. Graph analytics demonstrate their versatility and accuracy when making predictions, including evaluating the tightness of a friendship between two peers, identifying groups of malicious users in a large community, and finding optimum paths within a network while accounting for disruptions in real-time or even detecting anomalies.
We provide a wide range of services for all phases of the software development process. For long-term projects considering a substantial change to their data persistence environment, bespoke techniques can be devised. ETL development may be used to migrate data in or out of Neo4j (for example, into a GraphML format). Industry best practices for integrating and operating a graph database within a new or existing software architecture and infrastructure are followed. OpenCypher may be used to write optimal queries, but a GraphQL abstraction layer can be built on top.