System for real-time root cause analysis and automatic resolution in a multi-tenant e-commerce platform

Abstract

A system (100) for real-time root cause analysis and automatic resolution in a multi-tenant e-commerce platform, comprising: a) a real-time monitoring module configured to collect and monitor system performance metrics, transaction data, user activities and error logs from the Platform; (b) a root cause detection module powered by machine learning algorithms and configured to analyse the collected data to identify anomalies, detect patterns and determine the root cause of issues affecting the performance of the platform or the user experience; (c) an automatic remediation module configured to automatically trigger predefined remedial actions upon detection of a problem, including restarting services, scaling resources, reconfiguring systems, or switching to other service providers; (d) a cross-tenant adaptation module configured to ensure that root cause analysis and remediation actions are performed on a tenant-specific basis to isolate problems within individual tenants without impacting others; (e) an alerting and reporting module configured to generate real-time alerts and detailed root cause analysis reports for administrators and support teams, providing information about the identified problem, the actions taken to resolve it, and recommendations for future improvements; f) whereby the system automatically detects, diagnoses and resolves platform issues in real time, ensuring high availability and performance of the e-commerce platform with minimal manual intervention.

Description

[0001] The present invention relates to the field of e-commerce platforms, in particular to systems and methods for performing real-time root cause analysis and automatically resolving problems in multi-tenant e-commerce platforms.

[0002] The rapid growth of multi-tenant e-commerce platforms has created an environment where numerous merchants, customers, and backend services interact simultaneously. While this provides scalability and flexibility, it also results in a complex web of dependencies and interactions that can lead to performance issues, service interruptions, and errors. Due to the massive amounts of data and the multitude of possible causes, promptly detecting and resolving these issues becomes challenging.

[0003] Traditionally, e-commerce platforms rely on manual troubleshooting processes or generic alert systems, which often fail to provide timely or accurate diagnosis. As a result, issues such as service outages, slow transaction processing, or degraded user experience are not resolved efficiently, leading to downtime and customer dissatisfaction. Furthermore, these traditional methods often require significant human intervention, increasing operational costs and the risk of human error.

[0004] Modern e-commerce platforms require a smarter approach to problem management. Real-time monitoring combined with machine learning-based analytics could significantly reduce the time spent identifying root causes. The ability to automatically detect and resolve issues as they arise would increase platform reliability, improve user experience, and reduce the need for manual intervention.

[0005] Another critical challenge in multi-tenant environments is ensuring that an issue affecting one tenant doesn't impact others. Since tenants typically operate independently on the same platform, their systems must be isolated and protected from each other's issues. A solution that integrates tenant-specific monitoring and issue resolution would enable the platform to maintain stability across all tenants without causing cross-tenant disruption.

[0006] Furthermore, e-commerce platforms often face the challenge of scaling efficiently as more tenants are added. Systems for monitoring and resolving issues must be adaptable and scalable to meet increasing demand and complexity without compromising performance. This requires a system that can manage data from multiple sources, accurately detect issues, and dynamically implement solutions as platform usage increases.

[0007] The present invention addresses these challenges by providing a system that integrates real-time monitoring, root cause detection, automatic remediation, and tenant-specific problem resolution. It enables multi-tenant e-commerce platforms to maintain high availability and performance by automatically detecting, diagnosing, and resolving problems with minimal human intervention. The system increases operational efficiency, reduces downtime, and improves user satisfaction across the platform.

[0008] One objective of the present disclosure is to detect problems in real time to ensure immediate identification and response to platform disruptions.

[0009] Another objective of this disclosure is to provide automated problem resolution, reducing downtime by minimizing the need for manual intervention.

[0010] Another objective of the present disclosure is a scalable architecture that supports the efficient addition of new tenants without performance degradation.

[0011] Another objective of this disclosure is to isolate tenants to maintain platform stability by addressing issues specific to individual tenants.

[0012] Another subject of this disclosure is machine learning-based root cause analysis, which improves the accuracy of problem detection over time.

[0013] Another subject of this disclosure is the cost efficiency that reduces operating costs through the automation of troubleshooting and repair processes.

[0014] Another objective of the present disclosure is to provide an improved user experience that ensures uninterrupted service and consistent platform performance.

[0015] Another objective of the present disclosure is to reduce human error by automating complex troubleshooting and debugging tasks.

[0016] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0017] The present invention relates to a real-time monitoring module: It continuously collects performance and operational data from the platform, including system metrics, user activities, transaction data, and error logs. It also integrates with third-party services such as payment gateways, shipping providers, and customer support systems to ensure end-to-end transparency.

[0018] Another embodiment of the present invention is the Root Cause Detection Module, which analyzes the real-time data collected by the monitoring system to detect anomalies and identify potential root causes of platform issues. The root cause detection algorithm incorporates historical data, pattern recognition, and correlation analysis to diagnose issues such as slow response times, transaction errors, or service interruptions.

[0019] Another embodiment of the present invention is the Automated Remediation Module, which triggers an automated resolution process. Depending on the nature of the problem, the system can initiate predefined remediation actions, such as restarting services, reconfiguring system settings, reallocating resources, or notifying the appropriate support teams for further investigation. The remediation module utilizes self-healing scripts and decision trees to automate the resolution process without human intervention.

[0020] Another embodiment of the present invention is that in a multi-tenant environment, each merchant or customer on the platform may experience different issues. This module ensures that root cause detection and remediation processes are tenant-centric, meaning they can identify and resolve issues specific to individual tenants without impacting the operations of other tenants. It ensures that tenant data and processes remain isolated, so the platform remains stable even when resolving issues specific to specific tenants.

[0021] Another embodiment of the present invention is that the system generates real-time alerts for administrators, operators, and other relevant stakeholders when a problem occurs. It also generates detailed root cause analysis reports that provide information about the nature of the problem, the steps taken to resolve it, and possible improvements to prevent future problems.

[0022] The invention provides a system for real-time root cause analysis and automated resolution in multi-tenant e-commerce platforms, ensuring continuous platform performance and reliability. The real-time monitoring module collects and tracks system data, user activities, and external service conditions. The root cause detection module uses machine learning algorithms to detect anomalies and diagnose the causes of problems. Upon detection, the automatic remediation module triggers predefined actions to automatically resolve the issues. The multi-tenant adaptation module provides isolated resolutions for individual tenants, thus preventing disruptions for others. Finally, the alerting and reporting module generates real-time alerts and detailed reports to inform administrators and support teams about detected issues and actions taken.

[0023] The invention is explained again below with reference to the figure. It shows: Fig. 1 a system (100) for real-time root cause analysis and automatic problem resolution in multi-tenant e-commerce platforms.

[0024] Fig.shows a system (100) for real-time root cause analysis and automated remediation of issues in multi-tenant e-commerce platforms. The system for real-time root cause analysis and automated remediation in multi-tenant e-commerce platforms works by continuously monitoring the performance of the platform, infrastructure, and user interactions through a real-time monitoring module that collects a wide range of data, including system metrics, error logs, transaction details, and external service status. This data is fed into the root cause detection module, where advanced machine learning algorithms process and analyze it to identify patterns, discover anomalies, and correlate potential root causes of issues such as service outages, performance degradation, or transaction failures.Once the root cause is located, the automatic remediation module triggers predefined actions to resolve the identified issue, such as restarting services, scaling resources, reconfiguring system settings, or switching to another provider.

[0025] The multi-tenant customization module ensures that these processes are executed in a multi-tenant manner, meaning any action taken to resolve an issue in one tenant does not impact other tenants, thus maintaining the overall stability and isolation of the platform. Through the alerting and reporting module, the system also generates real-time alerts and detailed reports for administrators and support teams, informing them of the detected issue, the steps taken to resolve it, and suggestions for further improvements, thus minimizing manual intervention and ensuring rapid response to platform disruptions. This seamless integration of monitoring, analytics, and automated resolution enables the system to improve platform reliability, reduce downtime, and optimize the user experience across multiple tenants—all with minimal human involvement.

Claims

[1] A system (100) for real-time root cause analysis and automatic resolution in a multi-tenant e-commerce platform, comprising: (a) a real-time monitoring module configured to collect and monitor system performance metrics, transaction data, user activities and error logs from the Platform; (b) a root cause detection module powered by machine learning algorithms and configured to analyse the collected data to identify anomalies, detect patterns and determine the root cause of issues affecting the performance of the platform or the user experience; (c) an automatic remediation module configured to automatically trigger predefined remedial actions upon detection of a problem, including restarting services, scaling resources, reconfiguring systems, or switching to other service providers; (d) a cross-tenant adaptation module configured to ensure that root cause analysis and remediation actions are performed on a tenant-specific basis to isolate problems within individual tenants without impacting others; (e) an alerting and reporting module configured to generate real-time alerts and detailed root cause analysis reports for administrators and support teams, providing information about the identified problem, the actions taken to resolve it, and recommendations for future improvements; f) the system automatically detects, diagnoses and resolves platform issues in real time, ensuring high availability and performance of the e-commerce platform with minimal manual intervention. [2] The system (100) of claim 1, wherein the real-time monitoring module is further integrated with third-party services, such as payment gateways and shipping providers, to collect data from external sources and ensure end-to-end platform transparency. [3] The system (100) of claim 1, wherein the root cause detection module uses anomaly detection, regression analysis, and classification algorithms to identify correlations between various performance metrics and detected problems. [4] The system (100) of claim 1, wherein the automatic remediation module comprises predefined decision trees and self-healing scripts capable of automatically resolving service failures, performance bottlenecks, or transaction errors. [5] The system (100) of claim 1, wherein the multi-tenant adaptation module ensures that actions to address problems specific to one tenant do not impact the operation of the other tenants on the platform. [6] The system (100) of claim 1, wherein the alerting and reporting module provides real-time notifications to administrators and generates comprehensive root cause analysis reports detailing the detected problems, their impact, and the actions taken to resolve them. [7] The system (100) of claim 1, wherein the root cause detection module is continuously updated with historical data and patterns to improve its accuracy in detecting complex correlations and anomalies over time. [8] The system (100) of claim 1, wherein the automatic remediation module supports customizable remediation actions based on the severity and nature of the detected problem, including resource allocation, service restart thresholds, and switching to an alternative provider.

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