Technical Review: Tensor-Decomposition Stream Codec

Nagalla, Somesh (2025) Technical Review: Tensor-Decomposition Stream Codec. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 1051-1059. ISSN 2582-8266

The Tensor-Decomposition Stream Codec represents a revolutionary advancement in data compression technology for high-dimensional event streams. This innovative solution transforms how clickstream and IoT data are processed by leveraging tensor mathematics and GPU acceleration to achieve exceptional compression ratios while preserving data fidelity. Unlike traditional compression techniques that focus solely on row-wise redundancy, this codec treats data as multi-dimensional tensors, enabling it to identify and exploit complex patterns across user IDs, item IDs, and temporal features simultaneously. The architecture employs a sliding window approach with a lock-free CUDA kernel performing Tensor-Train Singular Value Decomposition, producing compact core tensors and factor matrices that significantly reduce data volume. These components integrate seamlessly with existing streaming frameworks and machine learning pipelines. The technology addresses critical challenges in modern data infrastructure including throughput bottlenecks, excessive energy consumption, and rising storage costs. By operating directly in the broker data path at production throughput levels, the codec delivers substantial performance improvements, energy savings, and operational cost reductions while enhancing analytical capabilities through direct integration with machine learning workflows.