An Efficient System for Large-Scale Collaborative Tasks in Government

Abstract

Abstract Government collaboration tasks are integral for grassroots governance and essential for government administration. Large-scale government collaboration tasks often involve multiple departments working together to solve complex tasks that require handling large amounts of data. However, existing offline processes make it difficult to manage complex collaboration scenarios and obtain timely execution results. We introduce the concept of a spatio-temporal task tree and propose a log-based incremental update method for updating statistical values in the tree structure to fulfill the need for real-time aggregation analysis of cross-temporal task execution situations. The spatio-temporal task tree fosters collaboration across various departments and organizational hierarchies to execute complex and large-scale tasks in government effectively. By flexible adaptations to different business models, this framework addresses the shortcomings of conventional systems in promoting inter-organizational collaboration. The log-based incremental update method for calculating statistical values within a tree structure allows for the updating of multi-dimensional data statistics in seconds. This approach effectively decreases computational expenses, enhances query efficiency, and improves real-time computation response. Results from experiments demonstrate the feasibility and high availability of the proposed approach, showcasing their potential to address the challenges associated with large-scale collaborative tasks in government affairs.

Keywords

• Government collaboration
• Spatio-temporal data
• Query optimization
• Key-value storage