Predictive Analytics in Resource Allocation for Digital Libraries

Abstract

Although there has been a change in the digital library landscape regarding content, demand, and even the lack of computing and storage bandwidth, all of this suggests a high requirement for the allocation algorithms. This study will focus on optimizing the demand for accessing digital libraries using predictive analytics. Machine learning algorithms can help analyze the access demand, peak usage, and operational prediction of content demand by relating to the history of the demand. Predictive analytics could be used to help in optimal dynamic resource allocation which is more resourceful in bandwidth use, less server workload and enhance efficiencies of content caching. The methodology of the algorithms applied in the current research will be time series forecasting, regression and classification algorithms as the most effective and accurate method or the best dynamic forecasting. In one case study of a university digital library, say, the effect of predictive analytics influenced the latency, user satisfaction and cost of operation. These effects are the possible forecasting analytics can do to enhance decision and resource designation in economics in the digital ecosystem. This research adds new knowledge to how digital libraries function and allocate information technology resources, combining intelligent allocation functions through predictive expectancies in creating a networked digital library. Future work will develop algorithms to allocate resources in real-time adaptive methods that are more efficient, responsive, and provide quality services to users.