Using a Model-driven Approach in Building a Provenance Framework for Tracking Policy-making Processes in Smart Cities
This addresses the problem of tracking policy-making processes for smart city analytics, but it appears incremental as it builds on existing provenance concepts without broad impact.
The authors tackled the lack of a framework for capturing provenance in smart city policy-making by developing the Policy Cycle Provenance (PCP) Framework using a model-driven approach, but no concrete results or numbers are provided.
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.