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.
翻译:30多年来,已经开发了用于知识发现、预测和管理的软件,然而,在利用现有项目开发和文物管理方法时,仍有一些尚未解决的疼痛点,历史上一直缺乏适用的方法,此外,应用的方法,如Agile,有一些限制,包括科学的不可伪造性,从而降低了其应用性。据证明,提出了植根于逻辑推理理念的发展方法,以及知识基础地貌学EKB。数据挖掘、机器学习和一般知识管理中的许多疼痛点在概念上得到了缓解。可以扩展为加速全球哲学探索、科学发现、教育以及知识共享和保留。EKB提供了一种将信息存储为知识、高于数据颗粒水平的一种解决办法,此外还讨论了计算机历史、软件工程、数据库、传感器、哲学以及项目和组织及军事管理中的相关专题。