Software has been developed for knowledge discovery, prediction and
management for over 30 years. However, there are still unresolved pain points
when using existing project development and artifact management methodologies.
Historically, there has been a lack of applicable methodologies. Further,
methodologies that have been applied, such as Agile, have several limitations
including scientific unfalsifiability that reduce their applicability. Evident,
a development methodology rooted in the philosophy of logical reasoning and
EKB, a knowledge base topology, are proposed. Many pain points in data mining,
machine learning and general knowledge management are alleviated conceptually.
Evident can be extended potentially to accelerate philosophical exploration,
science discovery, education as well as knowledge sharing & retention across
the globe. EKB offers one solution of storing information as knowledge, a
granular level above data. Related topics in computer history, software
engineering, database, sensor, philosophy, and project & organization &
military managements are also discussed.

提出了 Evident 方法论和 EKB 知识库拓扑结构来改善软件开发、预测和管理中的若干痛点，Evident 可以为哲学探索、科学发现、教育以及全球的知识共享和保留提供潜在加速。