Advanced Optimization for Big Data Streams with Quantum Insights for Real-time Big Data Analytics
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| Veröffentlicht in: | ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal vol. 14 (2025), p. e32876-e32891 |
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Ediciones Universidad de Salamanca
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| 001 | 3282913672 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2255-2863 | ||
| 024 | 7 | |a 10.14201/adcaij.32876 |2 doi | |
| 035 | |a 3282913672 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Acharya, Malika | |
| 245 | 1 | |a Advanced Optimization for Big Data Streams with Quantum Insights for Real-time Big Data Analytics | |
| 260 | |b Ediciones Universidad de Salamanca |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Big data analytics encounters scalability, latency, and privacy challenges, especially within real-time streaming contexts. We propose the Privacy-Aware Quantum Stream (PAQS), a distributed framework inspired by quantum principles, to overcome these obstacles. PAQS utilizes quantum superposition to effectively represent high-dimensional data, quantum entanglement for sophisticated correlation analysis and anomaly detection, and federated learning combined with homomorphic encryption to maintain privacy without compromising performance. The adaptive switching mechanism balances quantum-inspired and classical processing according to sensitivity and dimensionality criteria. Experiments are conducted on three datasets—OpenStreetMap, MIMIC-III, and KITTI, which show significant improvements: a throughput of 2. 53 TB/sec, a 60 % reduction in latency, an anomaly detection accuracy of 92. 3 %, and an 85. 4 % decrease in privacy violations when compared to baselines. These findings validate that PAQS provides consistent, secure, and scalable real-time analytics, positioning it as a strong solution for smart cities, healthcare, and autonomous transportation applications. | |
| 653 | |a Digital mapping | ||
| 653 | |a Transportation applications | ||
| 653 | |a Data transmission | ||
| 653 | |a Quantum entanglement | ||
| 653 | |a Big Data | ||
| 653 | |a Anomalies | ||
| 653 | |a Real time | ||
| 653 | |a Federated learning | ||
| 653 | |a Quantum mechanics | ||
| 653 | |a Privacy | ||
| 653 | |a Correlation analysis | ||
| 700 | 1 | |a Mohbey, Krishna Kumar | |
| 773 | 0 | |t ADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journal |g vol. 14 (2025), p. e32876-e32891 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3282913672/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3282913672/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |