Description:FIELD OF THE INVENTION
This invention relates to Auto-Tuning Framework for Scalable and Secure Stream Mining Using Traffic-Aware Load Distribution in Evolving Data Networks
BACKGROUND OF THE INVENTION
IoT networks, smart cities, stock exchange markets, and social networks today produce an enormous amount of real-time data. Processing and managing large data streams in real time and large scale is extremely challenging. Existing stream mining platforms are not scalable, cannot process random bursts of data, and are also prone to bottlenecks or loss of data during heavy traffic hours.
In addition, the majority of stream mining designs are data processing-oriented on data without inherent security in mind. The systems, consequently, are susceptible to data exploitation particularly in handling sensitive information such as user activity, transactions, or medical records.
The second is inefficient data loading. Policy or static partitioning by rules does not work when workloads dynamically shift by source, time, and location. This leads to over-loaded nodes, high latency, or idle available computing resources.
The innovation overcomes these limitations by providing a self-tuning, traffic-aware, and security-improved system. It records real-time patterns of information, forecasts workload changes, and dynamically re-allocates resources. Concurrently, it does stream-level encryption, providing data non-exposure at the cost of speed. This accumulates high accuracy, fault tolerance, and security for real-time analysis across distributed and dynamic databases.
Various data stream mining platforms appropriate for real-time data processing, like Apache Flink, Spark Streaming, and Storm, have appeared. These platforms enable high throughputs as well as fault tolerance but do not have natively deployed load balancing experience as well as static configuration requirements. They do not have natively deployed security components to integrate easily into the mining pipeline and are therefore insecure for highly sensitive applications.
Apart from that, the majority of current systems employ batch- or rule-based partitioning that is not dynamic in real time to accommodate changing traffic patterns. This causes the system to be overloaded or still inefficiently handling under changing workload or unexpected changes in workload.
It has also been suggested recently that the user utilize machine learning and load-aware systems but they are still highly dependent on an external control module and need to be re-trained extremely often. Such systems have also encountered scalability issues due to adherence to a geo-distributed database schema or migrating data nodes.
In contrast, the inventive subject matter integrates auto-tuning, traffic analysis, and security feature under a single architecture. It has a predictive feedback loop for auto-performance tuning without adjustment by humans. It also has data confidentiality by in-stream encryption, which is typically not addressed by the state-of-the-art.
The invention thus corrects rigid architecture, non-adaptability, and lack of security features in the state-of-the-art.
Legacy stream mining structures are always susceptible to varying volume of data, thus performance degradation or data loss. They completely rely on pre-decision partitioning schemes and never adaptive for dynamically varying workloads. Furthermore, the majority of the solutions always sacrifice inherent security in valuable mining in situations of real-time or sensitive data.
This invention otherwise is an auto-tuning, traffic-conscious, and secure system. The system continuously watches data flow and dynamically adapts by re-arranging its partitioning approach in real time. This eliminates bottlenecks, maximizes resource utilization, and enhances system responsiveness. With additional auto-tuning capability, it obviates manual parameter tuning and guarantees maximum throughput for varying loads.
In addition, the system also supports stream-level encryption to ensure data confidentiality and integrity during processing. This is particularly useful in smart governance, healthcare, and finance situations. The system can further be implemented using a modularity framework and therefore deployable on cloud and distributed systems. The system also supports latency-sensitive applications with predictive scaling of the mining nodes prior to their overloading.
Generally speaking, the invention offers a safe, scalable, and adaptive solution to conventional stream mining methods and therefore becomes especially relevant in the context of modern dynamic data structures.
US8468244B2: Distributed computer system processes data having select content (SC) represented by one or more predetermined words, characters, etc. The system has a plurality of SC data stores in a server cloud for respective security designated (Sec-D) data and granular data, each with respective access controls thereat. The data stores are operatively coupled over a network. An identification module identifies SC data and granular data stores for in the server cloud. A processor activates data stores in the server cloud thereby permitting access to the SC data and granular data stores based upon an application of access controls thereat. The processor has a reconstruction module operating as a data process employing the respective access controls to combine one or more of the Sec-D data and granular data.
US8370362B2: An improved human user computer interface system, wherein a user characteristic or set of characteristics, such as demographic profile or societal “role”, is employed to define a scope or domain of operation. The operation itself may be a database search, to interactively define a taxonomic context for the operation, a business negotiation, or other activity. After retrieval of results, a scoring or ranking may be applied according to user define criteria, which are, for example, commensurate with the relevance to the context, but may be, for example, by date, source, or other secondary criteria. A user profile is preferably stored in a computer accessible form, and may be used to provide a history of use, persistent customization, collaborative filtering and demographic information for the user. Advantageously, user privacy and anonymity is maintained by physical and algorithmic controls over access to the personal profiles, and releasing only aggregate data without personally identifying information or of small groups.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention relates to an auto-tuning framework designed for scalable and secure stream mining in evolving data networks. With the rapid growth of real-time data traffic across distributed environments, traditional stream mining systems face limitations in scalability, adaptability, and secure processing. Static configurations often fail to handle fluctuating loads and diverse traffic conditions, leading to performance bottlenecks and security vulnerabilities.
The proposed framework introduces a traffic-aware load distribution mechanism that dynamically allocates resources based on evolving network conditions. By integrating adaptive tuning and intelligent load balancing, the system ensures optimal utilization of computing resources while maintaining high throughput and low latency. Furthermore, the framework incorporates security modules that safeguard data streams during transmission and processing, mitigating risks such as unauthorized access and malicious data injection.
This integrated approach enhances the efficiency, scalability, and security of stream mining, making it particularly suitable for evolving data networks where traffic volumes and patterns are unpredictable.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention anticipates a smart, auto-tuning real-time data stream mining solution in dynamic database topologies. It optimizes traffic by continuous traffic monitoring and dynamic load-balancing techniques through adaptive partitioning technologies. It provides secure high-speed data stream processing with enhanced system performance under dynamic workloads. The invention includes traffic-aware modules, security-enriched mining engines, and smart resource allocation policies. With new heuristics added and feedback-optimal optimizations, it enables real-time learning, streaming decision-making, and scaling. It is precisely suited to be used in smart cities, money networks, and industrial-grade IoT applications where data generates data continuously, making stream processing inevitable for security and scalability. The above system makes the throughput, security, and correctness of data systems substantially improved.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The auto-tuning framework comprises multiple interconnected modules designed to optimize stream mining in distributed and evolving data networks. At its core, the system includes a traffic-aware load distribution module that monitors real-time traffic conditions and dynamically assigns computational tasks across nodes. This ensures balanced resource utilization, reduces congestion, and prevents bottlenecks.
An adaptive auto-tuning engine continuously evaluates system performance metrics such as throughput, latency, and resource usage. Based on these metrics, it automatically adjusts operational parameters, scaling resources up or down to match current traffic loads. This dynamic configuration capability eliminates the inefficiencies of static systems and provides resilience against fluctuating workloads.
To address data security challenges, the framework integrates secure transmission protocols and anomaly detection mechanisms. These ensure that sensitive data streams are protected against unauthorized access, tampering, or malicious interference while maintaining processing efficiency.
The stream mining module within the framework processes incoming data in real time, extracting patterns, insights, and anomalies. Its integration with the load distribution system ensures that mining tasks are evenly spread and executed with minimal delay.
Together, these modules create a robust framework that not only supports high-volume data streams but also adapts intelligently to network changes while safeguarding data integrity. The system is applicable to industries relying on continuous data analysis, including telecommunications, finance, healthcare, and IoT-driven environments.
The invention anticipates a smart, auto-tuning real-time data stream mining solution in dynamic database topologies. It optimizes traffic by continuous traffic monitoring and dynamic load-balancing techniques through adaptive partitioning technologies. It provides secure high-speed data stream processing with enhanced system performance under dynamic workloads. The invention includes traffic-aware modules, security-enriched mining engines, and smart resource allocation policies. With new heuristics added and feedback-optimal optimizations, it enables real-time learning, streaming decision-making, and scaling. It is precisely suited to be used in smart cities, money networks, and industrial-grade IoT applications where data generates data continuously, making stream processing inevitable for security and scalability. The above system makes the throughput, security, and correctness of data systems substantially improved.
The innovation in this patent is the fusion of traffic-aware partitioning with a self-adjusting automatically balancing stream mining engine that dynamically loads compute and security needs. This system doesn't statically configure like other systems, but rather self-tunes dynamically against changing database properties in real time. Predictive load forecasting and secure routing are used to intelligently distribute compute resources. Its auto-tuning module, which is in-built, is pre-trained with past traffic behaviors to auto-tune parameters automatically without any human touch. It also offers secure protection from data compromise through lightweight cryptographic processing at the data mining level. This kind of an amalgamation of self-tuning, scalability, and data-conscious security attributes it differently from other solutions and makes it a superior, real-time stream mining solution.
Variations or Alternative Embodiments
The innovation is adaptable and scalable on many various axes based on the deployment strategy and the application space.
Cloud-Native Edition: It may be installed as a microservices-based system over cloud infrastructure supporting Kubernetes. Auto-scaling node affinity and containers in such an instance can be leveraged to facilitate fine-grained calibration of resources.
Edge-Based Variant: A light variant can be developed near data sources in the case of IoT or edge application. Nodes nearer would be favored by the load balancer and would use light crypto modes for latency minimization.
GPU/FPGA Acceleration: In the case of high-frequency stock trade applications or video analysis scenarios, GPU-mined or FPGA-encrypted module can be added for faster processing.
Blockchain-Based Version: Blockchain technology can be employed to maintain access logs and secure transactions so that they become traceable and auditable, and is being implemented mainly in healthcare and finance.
Multi-Tenant Deployment: The application can be hosted on multi-user systems where resources can be dynamically allocated based on the priority or subscription of the user.
Each version maintains the critical functionality of auto-tuning, traffic-aware partitioning, and secure stream mining but with a purpose-specific module suitably tuned. All versions make the invention scalable and adaptive for fog, edge, and cloud computing platform.
Main Ingredients
Data Ingest Module: Real-time data stream ingester fed from sources including sensors, social streams, or financial streams.
Traffic Analyzer: Real-time data volume, velocity, and source distribution processing for predicting future traffic flows.
Auto-Tuning Engine: Automatic process parameter tuning like thread number, memory allocation, and partition number based on traffic feedbacks.
Load Balancer: Installs acquired knowledge by Traffic Analyzer to load work onto mining nodes in efficient and optimal manners.
Secure Stream Miner: Streams data with encrypted input. It employs light-weight encryption/decryption modules to process securely.
Storage Manager: Scales and stores results of mining in distributed file systems or databases and is thus fault-tolerant.
Monitoring & Alert System: Monitors system performance, detects outliers, and sends alerts for overloading or security breach.
Policy Manager: Allows admins to create security, performance, and SLA policies that guide auto-tuning choices.
Feedback Loop Controller: Enables learning from past traffic patterns and performance mining to influence future decisions.
User Interface & API Layer: Allows users to manage the system using utilization of dashboards and RESTful APIs to run analytics as well as configuration.
Technology
The innovation employs the blend of fresh software technologies in terms of algorithms, secure handling of data, and adaptive control systems to enable a system to provide efficient, scalable, and secure real-time stream mining.
•Software Algorithms:
no AUTOMATIC-TUNING Algorithm: Applying heuristics and reinforcement learning-based models to dynamically adjust parameters based on feedback from the system and history of traffic.
no Traffic Prediction Algorithm: Using time-series forecasting (e.g., ARIMA or LSTM) for predicting patterns in workload.
Secure Stream Mining: Utilizing optimized incremental decision tree or clustering algorithm with encryption integrated.
•Communication Protocols:
Low-latency data communication between distributed modules is enabled by RESTful APIs and WebSocket protocol.
oSecure transport is enabled using TLS/SSL supported by anonymizing data and hashing sensitive columns.
•Data Sources and Sensors:
oEdge-specific sensor type is handled by the system by integrating on IoT sensors, network monitors, and telemetry hardware via MQTT, CoAP, or HTTP.
•Power Supply
oEdge, cloud, or hybrid deployment modes with the requirement of shared AC/DC power supply or PoE in edge deployments.
• Security Mechanisms:
In-stream encryption support and lightweight symmetric encryption (AES-128) support.
Integrity and access control with support for optional blockchain modules or digital signatures.
• Platform Compatibility:
Docker, Kubernetes, Apache Flink, Apache Kafka, and AWS, Azure, or GCP cloud platforms support for deployment.
• Monitoring Tools:
Prometheus, Grafana, or custom dashboards are used by deployments to monitor resources and performance.
All the integration of technology makes the system homogeneous in a heterogeneous real-time data environment and self-auditing and secure.
Stepwise Working Functionality
1. Data Capture:
The system starts with processing real-time information from diverse input sources—IoT devices, APIs, logs, or streaming services—through light-weight communication protocols (e.g., MQTT, WebSocket).
2. Traffic Monitoring:
The Traffic Analyzer continuously monitors data traffic, classifying it according to the source, type, and size, and recording temporal trends for analysis.
3. Load Forecasting:
A forecasting model that predicts future data traffic (e.g., LSTM) anticipates future data traffic from historical trends. Such inputs are supplied to the Auto-Tuning Engine.
4. Auto-Tuning & Resource Allocation:
The Auto-Tuning Engine adapts process thread numbers, memory usage, and stream splits dynamically based on system state and predicted workload. Load Balancer distributes workload to processing nodes accordingly.
5. Secure Stream Mining
Secure Stream Miner processes encrypted data. It decrypts data stream, performs mining algorithms (e.g., stream clustering or classification), and re-encrypts output if required.
6. Storage and Delivery
Results are stored securely in distributed databases (e.g., Cassandra, HDFS) or flushed into client APIs in real-time.
7. Feedback Loop Performance:
There is a feedback loop to monitor latency, precision, throughput for enforcing reinforcement learning and automatically update parameters for the future.
8. Alerts & Logging:
The platform alerts on anomalies, overloading resources, or security issues with real-time alerts. All is audited and logged.
This is done through a method of generating a system dynamically scaling with workload behavior, processing in safety and high-throughput real-time observation through automated intervention.
FIVE Key Claims
1. Dynamic auto-tuning stream mining platform for self-configuring data processing parameters as expected through real-time traffic projections.
2. Combination of traffic-sensitive partitioning and load balancing mechanisms to maximize the processing power of distributed nodes.
3. Enable real-time extraction of data streams from on-board light weight encryption modules without degradation of system performance.
4. Auto-healing feedback loop of continuously evolving system behavior through learning and real-time statistics.
5. Modular architecture to enable cloud, edge, or hybrid deployment with scale-out communication and storage.
Environment, Society, Country Benefits
• Environment: Reduced power consumption by servers and the corresponding cooling requirement translate into energy efficiency and carbon savings.
• Society: Enhances health care, public safety, and traffic system decision-making by analyzing accurate, real-time data.
Maintains user privacy because of built-in security layers.
• Country: Enhances data infrastructure strength and analysis and unlocks industries like national defense, e-governance, smart cities, and disaster relief.
Can enable local AI platform development with limited foreign platform dependence.
Proper Functionality
The current invention is implemented as an intelligent, autonomous stream processing engine that dynamically observes data inflow, anticipates workload pattern predictability, auto-tunes internal knobs, and securely mines deep insights from the data. It dynamically scales out data to accessible compute nodes, optimizes resource efficiency, and introduces security measures in mining—without learning historical patterns. It is deployable across any data situation and network structure, thus it's a generic, rock-solid, and scalable mission-critical solution.
, Claims:1. An auto-tuning framework for scalable and secure stream mining in evolving data networks, comprising a traffic-aware load distribution module, an adaptive tuning engine, and integrated security mechanisms.
2. The framework as claimed in claim 1, wherein the traffic-aware load distribution module monitors network traffic and dynamically allocates computational tasks across nodes to balance workload.
3. The framework as claimed in claim 1, wherein the adaptive tuning engine adjusts system parameters based on performance metrics including throughput, latency, and resource utilization.
4. The framework as claimed in claim 1, wherein the framework incorporates secure data transmission protocols to protect data streams during processing.
5. The framework as claimed in claim 1, wherein anomaly detection mechanisms are integrated to identify and mitigate unauthorized access or malicious activity in data streams.
6. The framework as claimed in claim 1, wherein the stream mining module processes real-time data to extract patterns, anomalies, and insights with minimal latency.
7. The framework as claimed in claim 1, wherein the traffic-aware load distribution and stream mining modules are integrated to prevent bottlenecks and ensure efficient task execution.
8. The framework as claimed in claim 1, wherein the system dynamically scales resources up or down in response to fluctuating traffic conditions.
9. The framework as claimed in claim 1, wherein the adaptive tuning engine continuously learns from network behavior to improve performance over time.
10. The framework as claimed in claim 1, wherein the combination of traffic-aware load distribution, adaptive tuning, and integrated security mechanisms provides a novel approach to scalable and secure stream mining.