Enhancing Workflow with a Semantic Description of Scientific Intent

Peer reviewedPreprin

Utilising Provenance to Enhance Social Computation

Postprin

An adaptable system to support provenance management for the public policy-making process in smart cities

© 2018 by the authors. 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

Utilizing the blackboard paradigm to implement a workflow engine

Workflow management has evolved into a mature field with numerous workflow management systems with scores of features. These systems are designed to automate business processes of organisations. However, many of these workflow engines struggle to support complex workflows. There has been relatively little research into building a workflow engine utilizing the blackboard paradigm. The blackboard paradigm can be characterized as specialists interacting with and updating a centralized data structure, namely the blackboard, with partial and complete solutions. The opportunistic control innate to the blackboard paradigm can be leveraged to support the execution of complex workflows. Furthermore, the blackboard architecture can be seen to accommodate comprehensive workflow functionality. This research aims to verify whether or not the blackboard paradigm can be used to build a workflow engine. To validate this research, a prototype was designed and developed following stringent guidelines in order to remain true to the blackboard paradigm. Four main perspectives of workflow management namely the functional, behavioural, informational and operational aspects with their quality indicators and requirements were used to evaluate the prototype. This evaluation approach was chosen since it is universally applicable to any workflow engine and thereby provides a common platform on which the prototype can be judged and compared against other workflow engines. The two most important quality indicators are the level of support a workflow engine can provide for 20 main workflow patterns and 40 main data patterns. Test cases based on these patterns were developed and executed within the prototype to determine the level of support. It was found that the prototype supports 85% of all the workflow patterns and 72.5% of all the data patterns. This reveals some functional limitations in the prototype and improvement suggestions are given that can boost these scores to 95% and 90% for workflow and data patterns respectively. The nature of the blackboard paradigm only prevents support of only 5% and 10% of the workflow and data patterns respectively. The prototype is shown to substantially outperform most other workflow engines in the level of patterns support. Besides support for these patterns, other less important quality indicators provided by the main aspects of workflow management are also found to be present in the prototype. Given the above evidence, it is possible to conclude that a workflow engine can be successfully built utilizing the blackboard paradigm