Describes a data‑ingestion and caching mechanism using graph path cache structures to support low‑latency real‑time fraud or analytics pipelines

Abstract

In today's world of rapid fraud detection and analytics, systems need to be able to process and respond to large amounts of data in milliseconds. However, traditional data intake and caching pipelines don't meet these ultra-low-latency needs because they read data in order, do the same computations twice, and don't store data in the best place. These limitations make decision-critical systems, such as payment monitoring or behavioral analytics, have unnecessary bottlenecks. Even a delay of a millisecond might impair accuracy or risk reduction. To fix this problem, we provide a mechanism to store and use data that uses a graph route cache structure. This structure shows information & cache links as traversable graph routes instead of flat or hierarchical caches. This method treats each piece of information as a node that is linked to many other nodes by context-aware connections. This makes predictive & dependency-based caching possible. The ingestion system skillfully matches incoming data streams to this graph, which makes it easy to look things up & keeps the cache up to date without having to recalculate. When the latest data point comes in, the system finds the right graph paths & only gets and updates the nodes that are affected, instead of reloading complete datasets. Graph-based caching in the intake layer makes it easier to get their information that changes depending on the context & makes sure that the system stays in sync with changing actual time inputs. Tests show that this the latest cache design cuts query latency by 40% & doubles performance compared to previous cache setups. This technology combines fast intake with smart caching to create their scalable, low-latency data pipelines that may be used for more continuous fraud detection, actual time analytics & adaptive machine-learning processes