Paypal, Inc.

Ke Zheng

Fei Li

Xiaohan Yun

Zhenyin Yang

Yang Yu

Meng Zang

Wei Zhang

Jun Zhang

Jun Yang

Chun Kiat Ho

Lin Song

Kai Xie

Yuehao Wu

Yunfeng Li

Ying Yue

Uri Arad

Zhijun Ling

Haoran Zhang

Quin Zuo

Junshi Guo

Zhe Huang

Vadim Kutsyy

Chaoyi Chen

Wen Lu

Chinmayee Rathi

Mikhail Kourjanski

Siddarth Anand

Wenbing Zhu

Qinghai Fu

Tingjie Jia

A computer system receives a set of user-defined rules are that useable by a computer service to automate a decision flow. The computer system generates a graph model from the user-defined rules. From the graph model, the computer system determines an input dependency model that is indicative of a set of inputs referred to in the graph model. The input dependency model is useable by an orchestrator to coordinate accesses to the one or more data stores in which the set of inputs is stored. The computer system receives the set of inputs and determines one or more automated decisions by applying the set of inputs to the graph model.

Automated decision platform

Systems and methods that describe a graph database system with an online component and an offline component, are provided. Write events that modify a first graph in a real-time graph database included in the online component are received. Graph logs that include changes to the first graph in the real-time graph database caused by the write events are generated. The graph logs are transmitted to an offline component of the graph database system in a chronological order. A second graph in the offline component is modified using the graph logs. The first graph and the second graph are instantiated using a graph schema.

Graph database system

Methods and systems for processing incoming data in real-time are described herein. The incoming data is processed in real-time to generate data records that may be improved over time, for example, by automatically correcting inaccurate data in the records. In some embodiments, when data is received, a real-time process is initially performed on the received data under a first time constraint to produce first data for a data record. Subsequently, one or more non-real-time processes are then performed on the received data under a second time constraint to produce second data for the data record. The second data may be used to update the data record, for example, to correct any inaccuracy caused by the real-time process of the received data. Preferably, the second time constraint is longer than the first time constraint.

Self-healing real-time data processing

Growing community of inventors

Shanghai, China

Ke Zheng