Using a Model-driven Approach in Building a Provenance Framework for Tracking Policy-making Processes in Smart Cities

TL;DR

This paper introduces the Policy Cycle Provenance (PCP) Framework, a novel model-driven system designed to capture and track the policy-making process in Smart Cities, addressing existing gaps in provenance frameworks.

Contribution

It presents a new, adaptive provenance framework specifically tailored for policy-making in Smart Cities, utilizing a model-driven approach and networking techniques.

Findings

Designed the PCP framework for policy provenance tracking

Demonstrated the framework's adaptability to policy design challenges

Proposed a networking approach for workflow management

Abstract

The significance of provenance in various settings has emphasised its potential in the policy-making process for analytics in Smart Cities. At present, there exists no framework that can capture the provenance in a policy-making setting. This research therefore aims at defining a novel framework, namely, the Policy Cycle Provenance (PCP) Framework, to capture the provenance of the policy-making process. However, it is not straightforward to design the provenance framework due to a number of associated policy design challenges. The design challenges revealed the need for an adaptive system for tracking policies therefore a model-driven approach has been considered in designing the PCP framework. Also, suitability of a networking approach is proposed for designing workflows for tracking the policy-making process.