Evident: a Development Methodology and a Knowledge Base Topology for Data Mining, Machine Learning and General Knowledge Management

TL;DR

Evident is a new development methodology based on logical reasoning and a knowledge base topology designed to improve data mining, machine learning, and knowledge management by addressing existing limitations and pain points.

Contribution

It introduces a novel methodology and knowledge base topology that enhance knowledge discovery and management, overcoming limitations of existing approaches like Agile.

Findings

Conceptual alleviation of pain points in data mining and ML

Potential extension to accelerate scientific and philosophical exploration

Discussion of related topics across multiple disciplines

Abstract

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.