Paypal, Inc.

Pengshan Zhang

Yu Zhang

Jun Zhang

Xiaohan Yun

Haoran Zhang

Xin Li

Ying Yue

Junshi Guo

Xia Zhang

Jiaxin Fang

Fei Li

Quin Zuo

Xiaojun Luan

Wei Zhang

Jun Li

Xin Wang

Xin Xu

Chao B Zhang

Chun Kiat Ho

Lin Song

Zhenyin Yang

Kai Xie

Yuehao Wu

Thomas Anthony Doran

Karl Anton Hennig

Junhua Zhao

Ying Lin

Zhe Huang

Rey Neo

Meng Zang

Kun Wang

Yuliang Zhou

Junsheng Tan

Xin Chen

Haoyang Jia

Renhua Liang

Yao-Tseng Tseng

Jerry Tso

Changle Lian

Yangxing Liu

Zohar Li Marad

Delin Liu

Alon Wiener

Lance Lian

There are provided systems and methods for pairwise graph querying, merging, and computing for account linking. A service provider may provide an account graph system to identify pairwise similarities between different accounts based on shared data that may be identified through one or more linking characteristics. When providing pairwise graph similarities, a service provider may receive a query identifying two or more accounts and/or an account with a parameter for graph exploration and querying. The service provider may utilize connection, link, or relationship graphs, queried and generated using a graph database, to determine pairwise similarities between the designated seed account and one or more selected accounts. The graph may include vertices for different queried data points and edges connecting such queries, where directionality of the edges or other vectors may be used to identify links or hops between accounts for data querying and exploration.

Pair-wise graph querying, merging, and computing for account linking

Techniques are disclosed for detecting risk via a server system that receives, based on user activity at a device, an indication of a trigger event corresponding to a potential electronic communication. After receiving the trigger event indication and prior to receiving an indication of initiation of the electronic communication, the system executes a set of computations for the communication, including performing a multi-hop query to a graph database storing a graphical representation of a plurality of communications and storing results of the execution. In response to initiation of the electronic communication, the system retrieves, using information corresponding to entities involved in the electronic communication, one or more portions of the results of executing the set of computations the database. The system determines, based on the retrieved results, whether to approve the electronic communication. The disclosed techniques may advantageously decrease unsecure electronic communications relative to real-time risk detection techniques.

Graph computing for electronic communication risk detection

A method for operating a graph database, including receiving, by a computer system, a query to a particular graph database, the query identifying a plurality of vertices of the particular graph database. The method further includes performing, by the computer system, hash operations on two or more of the plurality of vertices to generate respective hash values and dividing, using the respective hash values, the query into a plurality of sub-queries, each corresponding to a subset of the plurality of vertices. The method also includes sending, by the computer system, ones of the plurality of sub-queries to a plurality of database repositories for the particular graph database.

Dual write and dual read access to graph databases

Methods and systems are presented for assisting a user to identify and evaluate features for use in a machine learning model configured to perform a task. Based on graph data associated with a graph data structure, a user interface is provided on a device. Based on user inputs received via the user interface, a feature candidate for the machine learning model is determined. The feature candidate is associated with a particular way of traversing the graph data structure to obtain attribute values associated with one or more vertices and/or one or more edges in the graph data structure. Based on the attribute values, a value corresponding to the feature candidate can be calculated. The value can be used to evaluate the effectiveness of the feature candidate in performing the task. The feature candidate can then be incorporated into the machine learning model as one of the input features.

Graph-based feature engineering for machine learning models

Techniques are disclosed for storing and retrieving large amounts of data in a non-relational database using sort keys. A server computer system may receive a request for raw data specifying a start timestamp and an end timestamp. The server determines a start key and an end key for performing a query on a distributed non-relational database storing key-value pairs, where the determining is based on the start timestamp and the end timestamp. The server may compare the start key and the end key to a sort key included in row keys of key-value pairs stored in the non-relational database. Based on the comparing, the server retrieves one or more rows of raw data from the non-relational database. The server generates a graphical representation of the one or more rows of raw data retrieved from the non-relational database. The disclosed techniques may advantageously improve the efficiency of a database management system.

Database management using sort keys

Techniques are disclosed for storing an arranging data in a database. A method includes a computer system storing, in a database, data indicative of a graph data structure having a plurality of nodes connected by a plurality of edges. The method further includes the computer system determining that a number of edges connected to a first node satisfies a threshold number. In response to the determining, the computer system may store an index in an index row associated with the first node. The index identifies a first row having first and second ranges of values stored in first and second rows, respectively. The values in the first and second rows correspond to edges connected to the first node. The values in the first and second ranges are usable to indicate properties of corresponding ones of the plurality of edges.

Graph storage in a database

Aspects of the present disclosure involve systems, methods, devices, and the like for generating highly scalable temporal graph databases. In one embodiment, a novel architecture is presented that enables the identification of mutation or changes in graphs. For the identification a combination of graph-based modeling and journal entry is used. Events occurring are consumed and changes are ingested, transformed for use by a graph simulation system. The changes are journaled using a vertex centric temporal journaling schema.

System and method for generating highly scalable temporal graph database

Methods and systems are presented for testing software applications in a production-like environment that simulates real-world failures of production environments. A production environment has production applications and databases configured to process user requests from users for conducting transactions with a service provider. A testing system provides an intermediate interface that enables a software application operating in the test environment to access at least one of a production application or a production database. The intermediate interface can be configured based on different failure configurations to simulate production component failures in the production environment. Failure injection and randomized failure modes can be employed, including for network-related failures (latency, dropped packets, connections, etc.) that might occur in the production environment.

Systems and methods for testing software applications

A method may include receiving instructions to process a first application in response to a user request. The method also includes determining whether to store the first application in a first processing queue or a second processing queue based on a comparison between a CPU processing cost associated with the first application and a GPU processing cost associated with the first application. Further, the method includes grouping a first set of applications stored in the first processing queue according to CPU grouping criteria and grouping a second set of applications stored in the second processing queue according to GPU batching criteria. The method also includes causing a CPU to process the grouped first set of applications and a plurality of GPUs to process the grouped second set of applications.

Scheduling applications in CPU and GPU hybrid environments

Systems and methods for query large database records are disclosed. An example method includes: selecting a user-specified test program for execution; determining one or more testing parameters associated with the user-specified test program; identifying, based on the one or more testing parameters, computing nodes within a distributed computing system; generating, based on the user-specified test program, a first plurality of computing tasks for execution by the computing nodes. The first plurality of computing tasks, when executed, generates a second plurality of computing requests to a server under test. The method also includes distributing execution of the first plurality of computing tasks across the computing nodes; and testing the server under test by causing the first plurality of computing tasks to be executed by the computing nodes.

Simulating a production environment using distributed computing technologies

Growing community of inventors

Shanghai, China

Pengshan Zhang