System for collecting and analyzing big data in real time

Abstract

A system (100) for real-time big data collection and analysis, comprising: a) a data input module configured to receive data streams from a plurality of distributed and heterogeneous data sources in real time; b) a data normalization and preprocessing module operatively coupled to the data input module and configured to cleanse, transform, standardize, and synchronize the incoming data; c) a metadata management and cataloging module configured to store, update, and manage metadata associated with each data stream, including source identifiers, schema, timestamps, and data quality indicators; d) a real-time analytics and stream processing module operatively coupled to the preprocessing module and configured to perform stream-based computations, event detection, and continuous analytics on the processed data;e) an artificial intelligence and machine learning integration module configured to interface with the analytics module to provide model inference, learning, retraining, and prediction output in real time; f) a visualization and reporting module configured to present processed data and analytics results through dynamic dashboards, visual elements, and automated reporting mechanisms; and g) a security, access control, and compliance module integrated with all other modules and configured to enforce user authentication, authorization, data encryption, and regulatory compliance; h) where the modules are configured to operate synchronously in a cloud-native environment to enable end-to-end, real-time big data analytics with minimal latency.

Claims

[1] A system (100) for the collection and analysis of big data in real time, comprising: (a) a data input module configured to receive data streams from a plurality of distributed and heterogeneous data sources in real time; (b) a data normalisation and pre-processing module operatively coupled to the data input module and configured to clean, transform, standardise and synchronise the incoming data; (c) a metadata management and cataloging module configured to store, update and manage metadata associated with each data stream, including source identifiers, schema, timestamps and data quality indicators; (d) a real-time analysis and stream processing module operatively coupled to the pre-processing module and configured to perform stream-based calculations, event detection and continuous analysis on the processed data; (e) an artificial intelligence and machine learning integration module configured to interface with the analytics module to provide model inference, learning, retraining and prediction output in real time; (f) a visualisation and reporting module configured to present processed data and analysis results through dynamic dashboards, visual elements and automated reporting mechanisms; and g) a security, access control and compliance module integrated with all other modules and configured to enforce user authentication, authorization, data encryption and regulatory compliance h) the modules are configured to operate synchronously in a cloud-native environment to enable end-to-end, real-time big data analytics with minimal latency. [2] The system (100) of claim 1, wherein the data input module supports streaming protocols including, but not limited to, Kafka, MQTT, HTTP, and WebSocket for real-time data collection. [3] The system (100) of claim 1, wherein the data normalization and preprocessing module uses AI-based algorithms to detect anomalies and impute missing data values. [4] The system (100) of claim 1, wherein the metadata management and cataloging module comprises a searchable data catalog with features for tagging, tracking provenance, and logging user access. [5] The system (100) of claim 1, wherein the real-time analytics and stream processing module supports complex event processing (CEP) and user-defined rules for real-time decision making. [6] The system (100) of claim 1, wherein the AI and machine learning integration module enables seamless deployment and management of machine learning models built with frameworks such as TensorFlow, PyTorch, or Scikit-learn. [7] The system (100) of claim 1, wherein the visualization and reporting module provides customizable dashboards with real-time KPI monitoring and role-based data access. [8] The system (100) of claim 1, wherein the security, access control, and compliance module includes support for GDPR, HIPAA, and CCPA compliance through automated policy enforcement and audit trails. [9] The system (100) of claim 1, wherein the cloud-native environment comprises containerized microservices and orchestrated deployments using Kubernetes or equivalent frameworks. [10] The system (100) of claim 1, wherein all modules are designed to support horizontal scaling and failover mechanisms to ensure high availability and reliability under large data loads.

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