Technology Category
Data Mining & Knowledge Management: Data Stream MiningMachine Learning: Time-Series/Data StreamsSearch and Optimization: Distributed Search
Data Dams: A Novel Framework for Regulating and Managing Data Flow in Large-Scale Systems
🤖 AI Summary
To address the challenges of real-time regulation, congestion susceptibility, and low resource utilization in dynamic data stream management under big data workloads, this paper proposes Data Dams—a novel adaptive data flow control framework inspired by physical dam systems. Its core innovation is an “intelligent gate” mechanism that integrates system-state awareness, time-series forecasting, feedback-driven flow control algorithms, and adaptive threshold scheduling, enabling closed-loop dynamic rate limiting under concurrent bandwidth, computational, and security constraints—thereby overcoming the limitations of conventional static-threshold approaches. Experimental evaluation demonstrates that Data Dams reduces average storage volume by 12.8%, increases total egress throughput by 3.25%, significantly improves system stability and real-time processing capability, and supports elastic scalability in large-scale distributed environments.
Technology Category
Application Category
📝 Abstract
In the era of big data, managing dynamic data flows efficiently is crucial as traditional storage models struggle with real-time regulation and risk overflow. This paper introduces Data Dams, a novel framework designed to optimize data inflow, storage, and outflow by dynamically adjusting flow rates to prevent congestion while maximizing resource utilization. Inspired by physical dam mechanisms, the framework employs intelligent sluice controls and predictive analytics to regulate data flow based on system conditions such as bandwidth availability, processing capacity, and security constraints. Simulation results demonstrate that the Data Dam significantly reduces average storage levels (371.68 vs. 426.27 units) and increases total outflow (7999.99 vs. 7748.76 units) compared to static baseline models. By ensuring stable and adaptive outflow rates under fluctuating data loads, this approach enhances system efficiency, mitigates overflow risks, and outperforms existing static flow control strategies. The proposed framework presents a scalable solution for dynamic data management in large-scale distributed systems, paving the way for more resilient and efficient real-time processing architectures.
Problem
Research questions and friction points this paper is trying to address.
Optimize dynamic data flow regulation
Prevent data congestion and overflow
Enhance large-scale system efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dynamic data flow regulation
Intelligent sluice controls
Predictive analytics optimization
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