Managing Data Complexity in Blockchain-Based Workflow Systems

Abstract

Blockchain-based workflow systems provide transparency, security, and data integrity. However, increasing data volume, variety, and velocity create complexity that can affect workflow efficiency, system scalability, and resource utilization. This study aims to develop a framework for managing data complexity while improving workflow efficiency through integrated data management and workflow optimization. A conceptual design approach is adopted by developing a layered architecture consisting of the data, blockchain, and application layers. The framework is conceptually evaluated through data complexity analysis based on volume, variety, and velocity and their effects on workflow efficiency and resource management. The proposed framework integrates data classification and prioritization, where high-priority data are processed on the blockchain, while low-priority data are stored off-chain. Data pruning, compression, smart contracts, and priority-based task scheduling are incorporated to reduce redundancy, optimize storage capacity, improve data access speed, and accelerate workflow execution. The integration of data management and workflow processes also helps minimize potential bottlenecks. Compared with previous studies, the framework provides a more integrated approach by combining data complexity management and workflow optimization within a single model. Conceptually, it has the potential to improve efficiency, scalability, and system effectiveness. In addition, the framework establishes a structured basis for future prototype development, simulation, and empirical testing across diverse blockchain workflow scenarios and operational conditions effectively. However, prototype implementation and empirical evaluation are required to quantitatively validate its performance.