2022/11/09 by Mingwu, Gao, Samer Haidar +1 · 1 citation
Business, Management and Accounting · Computer Science · #Agile software development #Artifact (error) #Artificial Intelligence (cs.AI) #Artificial intelligence #Big Data and Business Intelligence #Computer science #Data management #Data mining #Data science #Databases (cs.DB) #FOS: Computer and information sciences #Knowledge base #Knowledge engineering #Knowledge extraction #Knowledge management #Machine Learning (cs.LG) #Software #Software Engineering (cs.SE) #Software engineering #cs.AI #cs.DB #cs.LG #cs.SE
paper · pdf · doi:10.48550/arxiv.2211.10291
published in arXiv (Cornell University) (Cornell University)
arxiv created 2022/11/09 · openalex publication_date 2022/11/09 · arxiv updated 2022/11/21 · openalex created_date 2022/11/28 · openalex updated_date 2026/08/05
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.