Genetic Knowledge Persistence based Technology Trend Mining for Technology Opportunity Discovery

Abstract

For effective technology management within organizations, the capability to promptly and accurately analyze current technological information are essential to persist in today’s complex environments. Thus far, technology management processes – the identification, selection, acquisition, exploitation, and protection of technology (Gregory, 1995) – relied exclusively on technology domain experts’ qualitative technological analyses. However, given the current era of rapid technological advancements and need for an eclectic collection of knowledge from multiple disciplines, prior expert-based qualitative analysis approaches are difficult to provide aid for effective, fast-response, and flexible technology management endeavors. First, the increasing risk and difficulty of experts’ tasks in providing their intuition- and experience-based expertise is becoming more evident considering the speed and complexity of today’s technology change. To maintain up-to-date domain knowledge, even experts would be required to consistently scout, search, and absorb newly materializing knowledge. Second, it is rare for experts to be proficient in multiple domains. Current technology environments are interdisciplinary and requires experts to aid in managing convergent and emerging technologies. Employing experts in multiple disciplines may be a solution, but this approach is costly and may result in an inefficient return on investment, especially with the high amount of uncertainty and risk. Lastly, with the immense escalation of technical data being generated daily, the reliance on computerized solutions, rather than human-based analysis, is increasing. Therefore, development of a systematic technology opportunity discovery method that addresses these limitations and aids the identification and selection stages of technology management is proposed.