Paypal, Inc.

Srinivasan Manoharan

Vinesh Chirakkil

Yuehao Wu

Harish Nalagandla

Junhua Zhao

Gurinder Grewal

Christopher S Purdum

Girish Sharma

Sahil Dahiya

Jun Zhu

Chun Kiat Ho

Lin Song

Joydeep Hazra

Shivam Pandey

Xiaoying Han

Premila Viswanathan

Xiaohan Yun

Methods and systems are presented for providing a container orchestration framework for facilitating development and deployment of software applications across different operating environments within an enterprise system. Upon receiving a service request for processing a set of data is received, the container orchestration framework determines one or more machines that store the set of data. Instead of processing the set of data remotely, the container orchestration framework deploys a container that encapsulates an application on the one or more machines. Each application instance running on the one or more machines are executed to process a corresponding subset of data stored on the machine locally. The container orchestration framework obtains the output data from executing the applications on each of the one or more machines, and present the output data as a response to the service request.

Container orchestration framework

Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Self learning data loading optimization for a rule engine

Methods and systems are presented for providing concurrent data retrieval and risk processing while evaluating a risk source of an online service provider. Upon receiving a request to evaluate the risk source, a risk analysis module may initiate one or more risk evaluation sub-processes to evaluate the risk source. Each risk evaluation sub-process may require different data related to the risk source to perform the evaluation. The risk analysis module may simultaneously retrieve the data related to the risk source and perform the one or more risk evaluation sub-processes such that the risk analysis module may complete a risk evaluation sub-process whenever the data required by the risk evaluation sub-process is made available.

Systems and methods for providing concurrent data loading and rules execution in risk evaluations

A first graph that includes a plurality of containers is accessed. The containers each contain one or more rules that each have corresponding computer code. The containers are configured for sequential execution by a rule engine. The computer code corresponding to the one or more rules in each of the containers is electronically scanned. Based on the electronic scan, an interdependency among the rules is determined. Based on the determined interdependency, a second graph is generated. The second graph includes all of the rules of the containers, but not the containers themselves. At least some of the rules are configured for parallel execution by the rule engine.

Rule engine optimization via parallel execution

A unidirectional workflow execution engine is accessed. The unidirectional workflow includes at least a first node and a second node, where the first node is executed before the second node. A timeout setting of the first node is retrieved. An actual execution time of the first node is determined. A difference between the retrieved timeout setting and the actual execution time of the first node is calculated. A timeout setting of the second node is retrieved. The timeout setting of the second node is modified by adding, to the retrieved timeout setting of the second node, the calculated difference between the retrieved timeout setting and the execution time of the first node.

Optimizing timeout settings for nodes in a workflow

Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.

Automatic discovery of data required by a rule engine

Growing community of inventors

San Jose, CA, United States of America

Srinivasan Manoharan