Systems and Methods for Monitoring Performance of Payment Networks Through Distributed Computing

Patent metadata

Field	Value
Publication number	US-2016019534-A1
Application number	US-201514640535-A
Country	US
Kind code	A1
Filing date	Mar 6, 2015
Priority date	Jul 16, 2014
Publication date	Jan 21, 2016
Grant date	—

Abstract

Systems and methods for use in monitoring performance of payment networks through use of distributed computing. One example method includes generating metrics and/or events associated with a deployed region of the agent, correlating the metrics and/or events over at least one time interval, the time interval dependent on at least one of historical data related to the deployed region and a known event, detecting, at the agent, at least one variance in the metrics and/or events over the at least one time interval based on a statistical analysis with at least one tolerance, and publishing sampled data, to an associated collector, based on at least one of a sampling rule and the at least on variance.

First claim

What is claimed is:

1 . A method for use in distributing analysis, by a processing engine, for a payment network for processing payment transactions, the method comprising:
collecting, at the processing engine, sampled data from multiple agents, the multiple agents remote from the processing engine; determining at least one dependency based on the sampled data from at least one of the multiple agents and/or on at least one event received from the multiple agents; performing regression analysis on the sampled data; performing, at the processing engine, predictive analytics on the at least one dependency and the regression analysis; altering a remediation rule based on the predictive analytics, the remediation rule indicating at least one action to be taken by at least one of the multiple agents; and transmitting the remediation rule to the at least one of the multiple agents.

2 . The method of claim 1 , wherein collecting the sampled data includes collecting only new sampled data, in response to a data query, the new sampled data being new since a last data query.

3 . The method of claim 1 , further comprising deploying the multiple agents to each of multiple computing devices associated with the payment network.

4 . The method of claim 1 , wherein collecting the sampled data includes collecting the sampled data and other data from the multiple agents, via at least one collector; and
wherein the at least one dependency is not based on the other data.

5 . The method of claim 1 , wherein collecting the sampled data includes:
receiving, at a collector from the multiple agents, data related to the payment transactions; aggregating, at the collector, based on time and/or distribution of the multiple agents, the data, events received from at least some of the multiple agents, and/or a remedial action associated with at least one of the multiple agents; determining at least one variance based on at least one of the events received from the multiple agents; and publishing, by the collector to the processing engine, the at least one variance and the sampled data.

6 . The method of claim 1 , further comprising creating content aware clusters across multiple types and/or classes of the multiple agents and metrics associated with said multiple agents.

7 . The method of claim 1 , wherein altering the remediation rule includes appending the remediation rule to a set of remediation rules, said at least one remediation rule directing at least one of the multiple agents to route transaction data away from one or more other of multiple agents.

8 . The method of claim 1 , wherein each of the multiple computing devices is a point of sale terminal.

9 . A system for use in distributing performance analysis for a payment network, the system comprising:
one or more computing devices for connection to multiple agents associated with the payment network; the one or more computing devices including computer executable instructions embodied therein defining at least one collector and a processing engine; wherein the at least one collector is configured to:
receive, from multiple agents, sampled data relating to payment transactions; and
provide at least a portion of the sampled data to the processing engine; and
wherein the processing engine is configured to:
determine at least one dependency based on the sampled data received from the at least one collector and/or at least one event received from the multiple agents;
perform regression analysis on the sampled data;
alter a remediation rule based on the at least one dependency, the remediation rule indicating at least one action to be taken by the agent; and
transmitting the remediation rule to at least one of the multiple agents.

10 . The system of claim 9 , wherein the at least one collector is further configured to aggregate the sampled data and the at least one event and/or at least one remedial action associated with at least one of the multiple agents, and to determine at least one variance based on regression analysis of the sampled date and the at least one event and/or the at least one remedial action; and
wherein providing the at least a portion of the sampled data includes providing the at least one variance and the aggregated sampled data associated with the at least one variance; and wherein the at least one dependency is based on the at least one variance.

11 . The system of claim 10 , wherein the at least one collector is configured to aggregate the sampled data based on time and/or distribution of the multiple agents.

12 . The system of claim 9 , wherein the processing engine is further configured to perform predictive analytics based on a regression analysis of the received aggregated sampled data and to alter the remediation rule based on the at least one dependency and the predictive analytics.

13 . The system of claim 9 , wherein the one or more computing devices include a distributed storage memory data grid;
wherein the at least one collector is configured to store the aggregated sampled data in the distributed storage memory data grid.

14 . The system of claim 9 , further comprising multiple agents deployed at agent computing devices geographically distributed from the one or more computing devices.

15 . A computer-implemented method for use in distributing performance analysis for a payment network, the payment network including multiple computing devices distributed across a geographic region, the method comprising:
receiving, from multiple agents, sampled data relating to payment transactions; aggregating, at a collector, based on time and/or distribution of the multiple agents, the sampled data, events received from at least some of the multiple agents, and/or a remedial action associated with at least one of the multiple agents; determining, at the collector, at least one variance based on at least one of the events; and publishing, to a processing engine, the at least one variance and/or the aggregated data.

16 . The computer-implemented method of claim 15 , further comprising storing the at least one variance and/or the aggregated data in a distributed storage memory data grid.

17 . The computer-implemented method of claim 15 , further comprising creating content aware clusters across multiple types and/or classes of the multiple agents and metrics associated with said multiple agents.

18 . The computer-implemented method of claim 15 , wherein receiving the sampled data includes receiving only new sampled data, in response to a data query, the new sampled data being new since a last data query; and
wherein the method further comprises causing at least one of the multiple agents to perform a remedial action based on the sampled data and at least one remediation rule.

19 . The computer-implemented method of claim 15 , wherein the collector includes a device collector associated with multiple device agents; and
wherein each of the multiple device agents is deployed in a point of sale device.

20 . The computer-implemented method of claim 15 , wherein the collector includes a device collector associated with multiple network agents; and
wherein each of the multiple device agents is deployed in a commercial network server.

Assignees

• Mastercard International Inc

Inventors

• Sidhu Navjot Singh
• Hibbeler Craig
• Bhuvanagiri Vijayanath K
• Tsivtsivadze Revaz
• Dukkipati Narendra

Classifications

• G06Q20/38 Primary

  Payment protocols; Details thereof · CPC title

• G06Q20/4016

  involving fraud or risk level assessment in transaction processing · CPC title