Intelligent Approval of Access Control Flow in Office Automation Systems via Relational Modeling

2026-04-13

AI Summary

This work proposes a Relation Modeling–driven Intelligent Approval framework (RMIA) to overcome the inefficiency and lack of intelligent decision-making in traditional office automation systems that rely on manual, stepwise approvals. RMIA uniquely integrates binary (applicant–approver) and ternary (applicant–resource–approver) relationships into a unified model, enabling a multi-granular access control mechanism that progresses from coarse- to fine-grained authorization. The framework further incorporates an information fusion strategy and an automated decision algorithm to support intelligent approval routing. Evaluated on two real-world product datasets and validated through online A/B testing, RMIA demonstrates significant improvements in both approval efficiency and accuracy, confirming its effectiveness and practical applicability in enterprise environments.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Reasoning under Uncertainty: Relational Probabilistic Models
• Knowledge Representation and Reasoning: Applications

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Office automation (OA) systems play a crucial role in enterprise operations and management, with access control flow approval (ACFA) being a key component that manages the accessibility of various resources. However, traditional ACFA requires approval from the person in charge at each step, which consumes a significant amount of manpower and time. Its intelligence is a crucial issue that needs to be addressed urgently by all companies. In this paper, we propose a novel relational modeling-driven intelligent approval (RMIA) framework to automate ACFA. Specifically, our RMIA consists of two core modules: (1) The binary relation modeling module aims to characterize the coupling relation between applicants and approvers and provide reliable basic information for ACFA decision-making from a coarse-grained perspective. (2) The ternary relation modeling module utilizes specific resource information as its core, characterizing the complex relations between applicants, resources, and approvers, and thus provides fine-grained gain information for informed decision-making. Then, our RMIA effectively fuses these two kinds of information to form the final decision. Finally, extensive experiments are conducted on two product datasets and an online A/B test to verify the effectiveness of RMIA.

Problem

• access control flow approval
• office automation
• intelligent approval
• relational modeling
• automation

Innovation

• relational modeling
• intelligent approval
• access control flow
• office automation
• ternary relation