An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities
Journal Title: Informatics - Year 2018, Vol 5, Issue 1
Abstract
Government policies aim to address public issues and problems and therefore play a pivotal role in people’s lives. The creation of public policies, however, is complex given the perspective of large and diverse stakeholders’ involvement, considerable human participation, lengthy processes, complex task specification and the non-deterministic nature of the process. The inherent complexities of the policy process impart challenges for designing a computing system that assists in supporting and automating the business process pertaining to policy setup, which also raises concerns for setting up a tracking service in the policy-making environment. A tracking service informs how decisions have been taken during policy creation and can provide useful and intrinsic information regarding the policy process. At present, there exists no computing system that assists in tracking the complete process that has been employed for policy creation. To design such a system, it is important to consider the policy environment challenges; for this a novel network and goal based approach has been framed and is covered in detail in this paper. Furthermore, smart governance objectives that include stakeholders’ participation and citizens’ involvement have been considered. Thus, the proposed approach has been devised by considering smart governance principles and the knowledge environment of policy making where tasks are largely dependent on policy makers’ decisions and on individual policy objectives. Our approach reckons the human dimension for deciding and defining autonomous process activities at run time. Furthermore, with the network-based approach, so-called provenance data tracking is employed which enables the capture of policy process.
Authors and Affiliations
Barkha Javed, Zaheer Khan and Richard McClatchey
Self-Adaptive Multi-Sensor Activity Recognition Systems Based on Gaussian Mixture Models
Personal wearables such as smartphones or smartwatches are increasingly utilized in everyday life. Frequently, activity recognition is performed on these devices to estimate the current user status and trigger automate...
Designing towards the Unknown: Engaging with Material and Aesthetic Uncertainty
New materials with new capabilities demand new ways of approaching design. Destabilising existing methods is crucial to develop new methods. Yet, radical destabilisation—where outcomes remain unknown long enough that n...
Alt-Splice Gene Predictor Using Multitrack-Clique Analysis: Verification of Statistical Support for Modelling in Genomes of Multicellular Eukaryotes
One of the main limitations of the typical hidden Markov model (HMM) implementation for gene structure identification is that a single structure is identified on a given sequence of genomic data—i.e., identification of...
The Effect of Evidence Transfer on Latent Feature Relevance for Clustering
Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evi...
Health Literacy for the General Public: Making a Case for Non-Trivial Visualizations
Health literacy is concerned with the degree to which individuals can access and understand information to make health decisions. The multifaceted nature of health data presents challenges for individuals seeking to im...