An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities
Abstract
:1. Introduction
2. Methodological Approach, Background and Context
2.1. Methodological Approach
- (i).
- We have conducted a detailed investigation of the relevant literature to understand the policy domain and its challenges [11,18,19,20,21,22,23]; Section 2.2.1 covers the observations in detail. The study was also conducted in the domain of smart cities (details provided in Section 2.2.2) to explore the policy process in smart cities and how the policy process has (or is supposed to) transformed for its suitability in the smart cities context. The investigation uncovered important observations which are apposite to the design of a system that supports provenance management.
- (ii).
- We have elicited input from experts (policy makers) to understand the practical setup of the policy environment and to cross-check the observations that we have drawn from the literature (covered in Section 2.2).
- (iii).
- (iv).
- For testing our approach, we are collecting some policy formation examples from the Smarticipate project [24]. It is to be noted here that our system will not be fully deployed in the Smarticipate project; we simply aim at collecting policy examples from the project to verify our approach with the given examples.
2.2. Background and Context
2.2.1. Policy Process Domain Knowledge
Domain Challenges of the Process
- (i).
- Although policy-making shares some similar facets with business processes [11] it has been found that, unlike business processes, task specification is not straightforward and can be rather complex in policy-making.
- (ii).
- The process followed for devising policies is largely non-deterministic, unstructured and ad-hoc [22,23] in nature. This means that task specification and task assignment to actors largely depends on the policy and this consequently adds additional challenges in structuring processes. This comes about as a result of policy-making being a political process, with different policy demands and largely involves tacit knowledge of policy-makers to decide the next appropriate action [23].
- (iii).
- The relevant literature [11,18,19,20,21,23] states that knowledge intensive aspects are very much prevalent in the process of policy creation. Given this fact, the processes of devising policies are usually guided by human knowledge, experience and decisions. This largely contributes in an ad-hoc manner and leads to complex task specification (covered in points ‘i’ and ‘ii’ above).
- (iv).
- In comparison to a business process, the number of stakeholders involved in a policy process is normally significantly large [11,23,25]. These large number of stakeholders require a solution that facilitates cross-organisation communication. This leads to further challenges in process orchestration since actors involved in each policy may not be the same, thereby increasing the complexity of task identification and assignment in each policy. In the case of the specific assignment of roles and responsibilities to actors, the challenge of process orchestration remains because tasks and their sequence for each policy can vary. Furthermore, citizens can participate at any phase which may again increase the complexity associated with the policy process [11].
- (v).
- The policy formulation process is not the same for all policies [23]. Depending on the needs and requirements of policies, the tasks and the relevant stakeholders’ participation may fluctuate.
- (vi).
- Policy-making can be unpredictable [23] i.e., it is complex to orchestrate the process beforehand, due to the points ‘i,’ ‘ii,’ ‘iii,’ ‘iv’ and ‘v’ outlined above.
- (vii).
- Unlike a business process, a policy-making process may inherently be a lengthy, drawn-out process [11]. Often the execution of any particular task may last from a number of days to a couple of months. One of the factors of this lengthy process is the involvement of a large number of stakeholders. The consultation process with all the stakeholders contribute in lengthy policy-making process.
Characteristics of Policy Process
2.2.2. Smart City Concepts
- (a).
- Among many definitions of smart governance, we employ the description by Scholl [6] i.e., that smart governance serves as a foundation for smart government and fosters stakeholders’ participation and collaboration.
- (b).
- For smart government, we also use the description of Scholl [6] i.e., the use of ICT by government to manage and implement policies and use of smart governance principles for conceptualising smart government.
- (c).
- Our research proposes a solution in the context of smart cities. However, as noted earlier at present there exists no standard definition of a smart city and several descriptions fall under this [2]. For our work we focus on smart government, which employs ICT and smart governance objectives, thus providing a solution in the smart cities context.
2.3. Policy-Making Process in Smart Cities
2.4. Scope of Our Research
- (i).
- Policies are devised at various government levels. Our work is limited to local councils (also called local government) that operate at the city level.
- (ii).
- Our research focuses on smart government and smart governance aspects of smart cities.
3. Assessing the Suitability of Workflow Approach for Policy Provenance
3.1. Potential Workflow Approaches
3.2. A Critical Analysis of Workflow Approaches
4. The Proposed Network-Based Approach
4.1. The Rationale for a Network-Based Approach for Tracking Policy Cycle
4.2. Network-Based Approach
The Effectiveness of the Proposed Network Based Approach
4.3. The Known Shortcomings of the Proposed Network-Based Approach
4.4. Goal-Based Approach
4.4.1. The Rationale or Using a Goal-Based Approach
4.4.2. Goal-Based Approach for Structuring a Policy-Making Process
5. System Architecture
5.1. Working of the Architecture
- (i)
- the Goal Handling Layer implements the goal-based approach
- (ii)
- the Network layer implements the network approach
- (iii)
- the Interaction layer provides the interface with the actors
- (iv)
- the meta-model layer provides all details regarding the process, data, structure, provenance details. This layer uses the W3C PROV model [50] to specify the structure of the provenance information
- (v)
- Provenance analysis layer creates the provenance using tokens and as per meta-model layer
- (vi)
- Provenance recorder layer records the provenance
- (vii)
- Provenance Retrieval Layer is responsible for retrieval and display of provenance and
- (viii)
- Storage Layer that stores data and provenance (which we call as data store and provenance store). This layer is used by all layers either for storing or for retrieval of data.
5.1.1. Goal Handling Layer
5.1.2. Interaction Layer
5.1.3. Network Layer
5.1.4. Other Layers in the Architecture
5.2. Example to Demonstrate Functioning
6. Implementation Approach
7. Conclusions and Future Research Directions
Author Contributions
Conflicts of Interest
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Challenges | Complex Task Specification | Non-Deterministic | Knowledge Intensive | Large No. of Stakeholders (Including Citizens) | Different Policy Processes |
---|---|---|---|---|---|
Complex Task Specification | √ | √ | √ | √ | |
Non-deterministic | √ | ||||
Knowledge Intensive | |||||
Large No. of Stakeholders (including citizens) | |||||
Different Policy Processes | √ | ||||
Unpredictable | √ | √ | √ | √ | √ |
Lengthy | √ |
Challenges (From Section 2.2.1) | Description |
---|---|
Complex Task Specification | Network-based approach does not require pre-identification of activities and their sequence. Tokens are created at run-time which contain details of task specified by the actor (Table 1 shows that knowledge-intensive policy-making nature inputs in the challenge of tasks specification). Thus, our approach considers human input in task specification at the run-time. Actors specify the tasks as per policy demands which addresses challenge associated with pre-defining a process. |
Non-deterministic | Our approach does not consider construction and restructuring of process to guide human action. In our approach, humans guide the process (as non-determinism is due to knowledge-intensive nature as shown in Table 1) which is constructed on the fly from the collected provenance. |
Knowledge-intensive | No process is defined beforehand but human experience guides the process. Tasks (tokens) are created by policy-makers and routed to the concerned actor. The policy-makers analyses a given piece of information and knowledge at hand to define the next right action. |
Large no. of stakeholder | In our approach, tokens are carrier of information (this uplifts the challenge of defining a process that spans councils and organisations that are external to the council). Similar to IP packet switching, tokens carry information from one actor to another. While communicating with external authority, the local council keep record of who has been communicated with and from whom a response is required. No set defined process facilitates inclusion of several diverse stakeholders during the policy formation. Furthermore, this also promotes smart governance facets of including diverse stakeholders and citizens during policy-making. |
Different policy processes | Our proposed approach uplifts this overhead by not considering the process based approach and by taking into account human knowledge as a foundation for policy creation. |
Unpredictable (difficult to orchestrate process beforehand) | Network-based approach is not process-based thus addressing the challenge of process orchestration. However, policy process reconstruction from provenance data requires efficient algorithms. |
Lengthy process | Our system maintains state of all policies by tracking activities. These states help reinstate the policy process. |
Policy Process Characteristics (From Section 2.2.1) | Our Proposed Approach Solution |
---|---|
Characteristic i | The proposed network approach facilitates communication and collaboration with other stakeholders using a packet like communication. As policy process is largely manual therefore we assume in our approach that someone will have to enter provenance data into the system These details will then automatically be routed (by policy controller) to other stakeholders using the destination address in the packet. All the tokens generated as part of the process are saved in a database which provides an evidence of a process that was executed. All the information that is generated or processed (such as documents) will be stored in a database. |
Characteristic ii | Based on the human decision, the policy controller decides if policy process continues. |
Characteristic iii | As process-centric approach is not considered in our proposed solution. Therefore, loops are also not pre-defined. In case loop back is required then as per policy demand, re-execution of previous phase/or certain activities will be carried out. |
No. | Challenges of Network-Based Approach | Solution Provided by Goal-Based Approach |
---|---|---|
1 | No process state monitoring in a system | Goals are identified for each phase of policy cycle. These goals guide the policy-makers to carry out the tasks towards goals’ fulfilment. |
2 | System or process execution is less transparent | Goal-based approach provides an overview of which goals have been satisfied and which goal is currently under execution. |
3 | Initiation and termination of policy cycle and its phases | For each goal initiation and termination is specified. |
4 | Artefacts and approval bodies involved in a process cannot be stated in an ad-hoc environment | In policy-making process, approvals at various points are required from the concerned authorities. Therefore, artefacts and approval bodies’ information (where required) is associated with each goal of a policy cycle. |
5 | Provenance query | Process for each goal will be created at run-time but activities involved in a process contain ID of goal which associates the process to the goals. For identifying a sequence of tasks, sequence numbers (similar to packets’ sequence number) are associated with tokens. Using this approach, provenance can be queried using a goal ID. |
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Javed, B.; Khan, Z.; McClatchey, R. An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities. Informatics 2018, 5, 3. https://doi.org/10.3390/informatics5010003
Javed B, Khan Z, McClatchey R. An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities. Informatics. 2018; 5(1):3. https://doi.org/10.3390/informatics5010003
Chicago/Turabian StyleJaved, Barkha, Zaheer Khan, and Richard McClatchey. 2018. "An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities" Informatics 5, no. 1: 3. https://doi.org/10.3390/informatics5010003
APA StyleJaved, B., Khan, Z., & McClatchey, R. (2018). An Adaptable System to Support Provenance Management for the Public Policy-Making Process in Smart Cities. Informatics, 5(1), 3. https://doi.org/10.3390/informatics5010003