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

TL;DR

This paper introduces a relational modeling-driven framework to automate access control flow approval in office automation systems, reducing manual effort and improving decision accuracy.

Contribution

It presents a novel RMIA framework with binary and ternary relation modules for intelligent, automated access approval decision-making.

Findings

Effective in reducing approval time and manpower

Validated through experiments on product datasets and online A/B testing

Improves decision reliability in access control flow approval

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.