A lessons mining system for searching references to support decision making towards sustainable urbanization

The recurrence of similar problems caused by human errors in urbanization process is common throughout the world. However, the knowledge learnt from these problems should become lessons and important references for decision-making to avoid the recurrence of these problems, thus urban development can be sustainable. It is considered of imperative importance to incorporate the lessons experienced into the decision-making process in a way that can help foresee the potential problems and take proper measures for addressing the problems. There are few studies that have been conducted to investigate the similarity between the current scenario of urbanization practice and the previous context of lesson cases. The ignorance of this similarity presents a significant barrier for decision makers to learn from the existing lessons effectively thus to have references of how to make better decisions for future urbanization practices. This paper presents a Lessons Mining System (LMS) to assist in mining lessons experienced from previous practices. The establishment of LMS is based on Case-Based Reasoning (CBR) theory and the similarity matching principles. The system includes five components, namely, Lessons-case Representation, Lessons-case Store, Lessons-case Retrieval, Lessons-case Application, and Lessons-case Update. LMS can facilitate decision makers to understand what potential problems might occur from their current actions by referring to the lessons experienced previously in similar circumstances. This understanding can help decision makers take preventive measures against the potential problems. The use of LMS can send alarming messages to decision makers about what possible problematic consequence may occur, thus they can modify their actions before too late. A demonstration of Yangwu Town is presented to show the application of LMS, and the result shows that the lessons mined can provide valuable references for the government of Yangwu Town to improve their decision-making quality. (C) 2018 Elsevier Ltd. All rights reserved.