Asterinos1/Distributed-Skyline-Processing

The project implements well-known MapReduce-based skyline partitioning algorithms (MR-Dim, MR-Grid, MR-Angle) within a modern streaming framework (Apache Flink). While the technical problem—efficiently finding non-dominated points in high-dimensional streaming data—is non-trivial, the project shows zero signs of adoption (0 stars, 0 forks) after 100+ days. This suggests it is likely an academic project or a student implementation rather than a production-ready library. The 'skyline' problem is a niche area of database research often eclipsed by more general-purpose multi-objective optimization or vector search capabilities in modern OLAP engines. Defensibility is minimal as it lacks a community, documentation for production deployment, or proprietary optimizations. It is highly susceptible to displacement by more active Flink extensions or native SQL operators in platforms like StarRocks or ClickHouse that handle complex analytical queries. Frontier labs have little interest in this specific niche, focusing instead on broader reasoning and search capabilities.