The Collective Intelligent Design Ecosystem is a dynamic ecosystem founded on an online design platform that leverages collective intelligence to support the creation of novel products. The system's primary components are its users and designers. Maintaining the system's sustainability requires expanding the scale of the designer and user populations as it evolves to stabilize. However, the unity of ecological interactions between various populations is fragmented in contemporary studies of population-scale evolution, and the parameterization of evolutionary models is illogical. To overcome this gap, this research provides a population evolution model of collective intelligent design incorporating participants' intra- and interspecific ecological connections. The model's validity is verified by the evolutionary simulation of 110 designers and 5990 users of China's largest collective intelligence design platform, the Zhubajie platform, and illuminating conclusions are in turn drawn from this simulation. First, the designer's influence on the user is greater than the user's impact on the designer. Second, keeping current members engaged is more crucial to the system's viability than luring in new ones. Third, fostering collaboration among designers while retaining user competitiveness can promote system growth. Fourth, decreasing the reliance between particular designers and users might hasten the system's evolution.

Modern key-value stores often use write-optimized indexes and compact in-memory indexes to speed up read and write performance. One popular write-optimized index is the Log-structured merge-tree (LSM-tree) which provides indexed access to write-intensive data. It has been increasingly used as a storage backbone for many services, including file system metadata management, graph processing engines, and machine learning feature storage engines. Existing LSM-tree implementations often exhibit high write amplifications caused by compaction, and lack optimizations to maximize read performance on solid-state disks. The goal of this paper is to explore techniques that leverage common workload characteristics shared by many systems using key-value stores to reduce the read/write amplification overhead typically associated with general-purpose LSM-tree implementations. Our experiments show that by applying these design techniques, our new implementation of a key-value store, SlimDB, can be two to three times faster, use less memory to cache metadata indices, and show lower tail latency in read operations compared to popular LSM-tree implementations such as LevelDB and RocksDB.