Big data-driven enterprise management and market decision-making framework

Big data-driven enterprise management and market decision-making framework

연구 분야: Databases

학회: Service Oriented Computing and Applications

This paper presents a hierarchical structure for corporate management and big data-driven market economic decision-making. The framework is organized into five layers: interface, service composition, service, business component, and resource. To provide realistic solutions, the service layer employs reusable and standardized software components, as well as arrangements such as selection looping, and parallel processing. Key technologies such as service module architecture, business practice execution languages, service data objects, and enterprise facility bus systems have been incorporated to maximize system composition. Using an aggregation pool object replacement technique and object soft reference technology, the suggested framework increases query performance of vast multidimensional data, according to experimental results. The “aggregation pool object replacement strategy” optimizes resource use and performance by replacing older objects with newer, relevant data. This method is used in systems with large datasets. Object soft reference technology allows garbage collectors to recover objects as needed while keeping them in memory, aiding memory management in systems with large datasets. By bridging internal and external enterprise data, the framework enhances decision-making capabilities, enabling decision-makers to access richer resources across organizational boundaries. The integration fosters data-driven enterprise transformation. It advances management methods and supports market economic decision-making.