<![CDATA[The rapid digitization of personal and organizational workflows has exacerbated the phenomenon of digital hoarding, leading to critical storage overload, inefficient resource allocation, and reduced system performance in cloud-based environments due to passive data accumulation and inactive archive piling. Many users tend to preserve files indefinitely without proper categorization or deletion, causing unnecessary storage expansion and increased maintenance complexity over time. This study aims to develop LUMEN (The Living Archive), a novel intelligent file management system that implements an automated “data decay” algorithm to shift the burden of file organization from the user to the system itself. The proposed methodology integrates Data Lifecycle Management (DLM) principles mapped through Business Process Model and Notation (BPMN), while utilizing Python and Flask for backend processing and User Centered Design (UCD) principles to create an intuitive and accessible frontend interface. The system evaluates the temporal differential between current and last-accessed timestamps, translating file vitality into a gradually fading visual representation that provides psychological nudges encouraging users to review or remove neglected data. A 90-day storage optimization simulation was conducted to quantitatively evaluate the system’s effectiveness and operational efficiency. The results demonstrate that LUMEN’s automated purging and retention policies successfully eliminate redundant and inactive data, achieving a 70% reduction in storage capacity consumption compared to traditional static storage models. Furthermore, the system significantly minimizes manual intervention, human error, administrative workload, and computational overhead. The implications of this research indicate that automated organic archiving can effectively mitigate digital hoarding behaviors while maximizing resource utilization for sustainable data management practices. Future research should explore the integration of advanced compression methods, such as Goldbach Codes, alongside comprehensive user acceptance evaluation using the Technology Acceptance Model (TAM) to further improve usability, system comfort, and memory efficiency. Keywords— Digital Hoarding; Data Decay; User Centered Design; Cloud Storage Optimization; Data Lifecycle Management]]>
