Automated real-time event processing and scalable data management system for cloud-based applications

Abstract

A scalable, automated, real-time event processing and data management system for cloud-based applications, including: a) a data ingress module configured to receive and preprocess high-speed data streams from multiple sources, including IoT devices, web applications, and databases, where the preprocessing includes filtering, transformation, and enrichment of the incoming data; (b) an event processing module operatively connected to the data input module and configured to apply complex event processing techniques (CEP) to detect patterns, anomalies and correlations within the pre-processed data streams in real time; c) a scalable storage management module configured to store processed data using a hybrid model that includes distributed Nosql databases, data lakes, and object storage solutions, ensuring data durability, availability, and optimized retrieval performance; (d) a data analysis module operatively connected to the event processing module and the memory management module and configured to apply machine learning models, statistical algorithms and predictive analytics to extract actionable insights from the processed data; and (e) a monitoring and security module configured to continuously monitor system performance, detect anomalies, implement encryption protocols, enforce access controls and ensure data integrity and confidentiality; f) wherein the system is configured to dynamically allocate computing resources to maintain low latency and high throughput under varying data loads in distributed cloud environments.

Description

[0001] The present invention relates to an automated real-time event processing and scalable data management system designed for cloud-based applications. It enables the efficient ingestion, processing, and analysis of high-speed, low-latency data streams. The system ensures scalability, fault tolerance, and seamless integration into distributed cloud environments for optimized performance.

[0002] The rapid development of cloud-based applications and the proliferation of data-generating devices such as IoT sensors, mobile platforms, and web applications have created an unprecedented need for efficient, real-time data processing systems. Traditional data management systems, primarily designed for batch processing, are not suitable for applications requiring instant insights and low-latency decision-making. Existing systems often face challenges related to scalability, fault tolerance, and performance degradation when confronted with rapid data streams from diverse sources. Furthermore, they struggle to maintain data integrity and consistency while ensuring robust security and availability in distributed cloud environments.

[0003] Furthermore, integrating real-time analytics and processing capabilities into diverse cloud infrastructures remains a significant challenge. Current solutions are often complex to implement and lack the flexibility to adapt to dynamic workloads or seamlessly scale resources on demand. The inability to effectively process, analyze, and store large amounts of streaming data limits the applicability of existing systems in critical areas such as financial services, healthcare, industrial automation, and smart cities. Therefore, there is an urgent need for a scalable, automated, and robust system capable of performing real-time event processing, efficient data management, and advanced analytics in distributed cloud architectures.

[0004] One goal of this disclosure is to automatically adapt computing resources to efficiently handle different data loads.

[0005] Another objective of the present disclosure is to ensure immediate detection of patterns and anomalies through low-latency processing.

[0006] Another goal of this disclosure is to combine NoSQL databases, data lakes, and object storage for optimal performance and durability.

[0007] Another objective of this disclosure is to implement redundancy and replication strategies for high availability and reliability.

[0008] Another objective of this disclosure is to support machine learning and predictive algorithms for comprehensive insights.

[0009] Another objective of this disclosure is to ensure data integrity and confidentiality through encryption and access control mechanisms.

[0010] Another objective of this disclosure is to track system performance metrics to detect problems and maintain operational consistency.

[0011] Another objective of this disclosure is to provide intuitive dashboards and APIs for seamless accessibility and decision-making.

[0012] The present invention generally relates to a scalable, automated real-time event processing and data management system designed for cloud-based applications, enabling efficient ingestion, processing, analysis, and management of high-speed data streams from diverse sources such as IoT devices, web applications, and databases. The system comprises interconnected modules, including a data ingestion module that standardizes and preprocesses incoming data through filtering and enrichment and forwards the cleaned data to an event processing engine. The event processing engine applies complex event processing (CEP) techniques to detect meaningful patterns, aggregate relevant data, and trigger predefined actions with minimal latency.To ensure seamless scalability, the system dynamically allocates computing resources based on the incoming data load, optimizing throughput and performance. Processed data is stored either in a scalable storage management module that uses a hybrid storage model of distributed NoSQL databases, data lakes, and object storage for high availability and efficient retrieval, or directly fed into a data analytics module for advanced analytics using machine learning models and predictive algorithms. The monitoring and security module simultaneously monitors system performance, detects anomalies, and implements encryption and access controls to ensure data integrity, confidentiality, and reliability.The analysis results are visualized via integrated dashboards and APIs, providing users with accessible, actionable insights that support informed decision-making across various application areas.

[0013] The present invention relates to an automated real-time event processing and scalable data management system designed for the efficient processing of high-speed data streams in cloud-based applications. This system is designed to provide seamless integration, scalability, fault tolerance, and optimized performance by leveraging distributed computing resources. The system consists of several key modules that work together to provide robust and scalable data processing capabilities. Data Ingestion Module

[0014] The Data Ingestion Module is responsible for collecting and importing real-time data from various sources such as IoT devices, social media feeds, enterprise databases, and external APIs. This module supports batch and streaming data ingestion and enables compatibility with structured and unstructured data formats. It leverages scalable message queuing systems and distributed file storage mechanisms to ensure data integrity and availability during transit. By employing load balancing techniques, this module optimizes data ingestion rates, ensuring seamless processing of data bursts and maintaining low-latency operations. Event processing module

[0015] The event processing module is the heart of the invention and is responsible for the real-time processing and analysis of incoming data streams. It uses complex event processing (CEP) techniques to detect patterns, anomalies, and correlations in the data streams as they arrive. The engine uses a rule-based architecture that allows the user to define the event processing logic through declarative queries. It also includes machine learning algorithms for predictive analytics, anomaly detection, and decision-making tasks. The module is optimized for high throughput and low latency, making it suitable for mission-critical applications that require immediate responses. Data management module

[0016] The Data Management module provides scalable storage solutions for both transient and persistent data. It integrates distributed databases, data lakes, and in-memory storage systems, offering a hybrid storage architecture. This module ensures data consistency, redundancy, and fault tolerance through the use of advanced replication and partitioning techniques. It also supports the ACID (Atomicity, Consistency, Isolation, Durability) and BASE (Basically Available, Soft State, Eventual Consistency) transaction models, providing flexibility based on application requirements. Furthermore, the module includes data compression and indexing mechanisms to improve storage efficiency and retrieval performance. Scalability and load balancing module

[0017] The Scalability and Load Balancing module ensures the efficient distribution of computing tasks across cloud resources. It uses containerization and virtualization technologies to dynamically allocate resources as needed. The module continuously monitors system performance and adjusts resource allocation to avoid bottlenecks and ensure optimal throughput. Automatic scaling mechanisms are used to improve system responsiveness during peak loads while minimizing resource utilization during low-traffic periods. Furthermore, this module includes disaster recovery protocols to maintain business continuity in the event of failures. Security and authentication module

[0018] The Security and Authentication module provides robust mechanisms to ensure data protection, integrity, and authorized access throughout the system. It includes multi-factor authentication (MFA), encryption protocols, and secure communication channels to protect sensitive data. The module also supports role-based access control (RBAC) and attribute-based access control (ABAC) for effective user permission management. Regular security audits and anomaly detection algorithms are implemented to proactively detect and mitigate potential threats. Monitoring and analysis module

[0019] The Monitoring and Analytics module provides a user-friendly interface for visualizing and analyzing system performance in real time. It delivers comprehensive reports, metrics, and alerts on data ingress rates, processing latency, resource utilization, and anomaly detection results. This module supports customizable dashboards, allowing users to tailor the visualizations to their specific needs. It also provides predictive analytics tools for forecasting trends and optimizing system performance. API integration module

[0020] The API integration module enables seamless interoperability with external systems and services. It provides RESTful APIs, WebSockets, and gRPC interfaces for integrating the invention with third-party platforms. This module ensures compatibility with various communication protocols and data formats, thus improving the adaptability of the invention in various application areas. Furthermore, it provides mechanisms for managing API rate limits, access controls, and data transformations during transmission.

[0021] The invention is explained again below with reference to the figure. It shows: Fig. : Illustration of the basic features of the system for automated real-time event processing and scalable data management How the system works

[0022] Fig.illustrates the system's functionality, which begins with data ingestion, where various data sources continuously feed data into the data ingestion module. This module standardizes the incoming data formats, applies preprocessing steps such as filtering and enrichment, and passes the cleaned data to the event processing module. The event processing module then applies complex event processing rules to identify meaningful patterns, summarize relevant data points, and trigger predefined actions. This process is scalable, allowing the system to dynamically allocate computing resources based on the incoming data load, ensuring real-time processing with minimal latency.

[0023] After processing, the data is either stored in the scalable storage management module or fed directly into the data analytics module for further analysis. The storage module ensures high availability through replication mechanisms, while the analytics processes are continuously updated with the latest data to deliver accurate insights. Monitoring and security modules run concurrently, ensuring the entire system remains functional, secure, and optimized. Anomalous activity is detected and mitigated in real time to ensure consistent operational performance. Finally, the data analytics results are visualized and made available to users via dashboards or APIs to enhance decision-making capabilities.

Claims

[1] A scalable, automated, real-time event processing and data management system for cloud-based applications, comprising: a) a data ingress module configured to receive and preprocess high-speed data streams from multiple sources, including IoT devices, web applications, and databases, where the preprocessing includes filtering, transformation, and enrichment of the incoming data; (b) an event processing module operatively connected to the data input module and configured to apply complex event processing techniques (CEP) to detect patterns, anomalies and correlations within the pre-processed data streams in real time; c) a scalable storage management module configured to store processed data using a hybrid model that includes distributed Nosql databases, data lakes, and object storage solutions, ensuring data durability, availability, and optimized retrieval performance; (d) a data analysis module operatively connected to the event processing module and the memory management module and configured to apply machine learning models, statistical algorithms and predictive analytics to extract actionable insights from the processed data; and (e) a monitoring and security module configured to continuously monitor system performance, detect anomalies, implement encryption protocols, enforce access controls and ensure data integrity and confidentiality; f) wherein the system is configured to dynamically allocate computing resources to maintain low latency and high throughput under varying data loads in distributed cloud environments. [2] The system of claim 1, wherein the data input module further comprises load balancers to manage traffic spikes and ensure uninterrupted data flow without performance degradation. [3] The system of claim 1, wherein the event processing engine uses distributed stream processing frameworks selected from Apache Flink, Spark Streaming, or their equivalents to improve scalability and fault tolerance. [4] The system of claim 1, wherein the scalable storage management module implements redundancy and replication strategies to ensure fault tolerance and high availability in the event of hardware or network failures. [5] The system of claim 1, wherein the data analysis module is configured to generate visual representations of analysis results via integrated dashboards and APIs for improved user accessibility and decision making.

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