Predictive process and compliance monitoring is an area of research that is opening up a lot of challenges and new research directions. The main issues in this field are: how to predict whether the IT Business Process is likely to violate an SLA; how to detect the occurrence of such a violation; and the impact of the resulting mitigation action.
Predicting possible situations of SLA conformance and violation
For most businesses, the ability to deliver a good product or service on time, and on a budget is a high level of priority. The process can be complicated by a host of external and internal factors, so having a comprehensive suite of tools and processes in place can make all the difference. A solid compliance program can be the most valuable asset in any organization.
A typical SLA target is a building permit request that must be completed within three months. The process involves a series of steps, each of which entails the proper acronym. If an error occurs, the consequences can be dire. A robust internal control mechanism can help eliminate the risk of a major setback. An automated audit function can also keep track of minor mishaps. Using a standardized approach will reduce the cost of implementing a robust compliance program. Using this software will ensure the most important tasks are completed on time and on budget.
While no one can claim to have perfected the craft, the right approach is essential. This is not only to keep customers happy, but it also helps to avoid a lawsuit and avoid negative press. As such, a good SLA system should include a robust compliance program, a robust and well-trained staff, and a robust system of checks and balances.
Impacts of mitigation actions on compliance monitoring in IT Business Processes
Compliance monitoring is a very important activity. It helps to detect possible compliance violations and to predict future violations. The objective of this activity is to provide meaningful feedback to the user.
To provide the most suitable support for compliance monitoring, it is essential to identify the most relevant elements of the problem. There are a number of approaches that focus on specific aspects. Some of them address the question of general architecture, while others are based on language aspects. However, there is no single approach that addresses the issue of proactive management.
In most of the current approaches, the concept of monitoring is mainly concerned with detecting violations in real-time. In addition, they usually rely on the use of fine-grained compliance rule language. But there is no standard framework to assess the efficacy of this approach. Therefore, it is difficult to compare and contrast compliance monitoring systems.
A compliance monitoring framework should include a set of metrics, and summarize detailed information about each compliance rule. It should also provide aggregated feedback to the users.
A common metric is the compliance degree. The “degree” of compliance is a measure of how well a compliance rule is implemented. This can be calculated from the number of violations.
Open challenges and research directions for predictive process and compliance monitoring
Business process compliance refers to the analysis of events that could lead to compliance violations. Compliance monitoring provides fine-grained feedback and predicts the activities that will be executed in the future. This is important for reactive countermeasures to detect and correct possible violations.
In this paper, we introduce a framework for systematically comparing compliance monitoring approaches. The proposed framework identifies the functionalities that are essential for business process compliance. It is also aimed at supporting the use of existing tools. We highlight the need for further research and development to provide support for compliance violation detection during runtime and for providing recommendations to the end user.
First, we define the ten compliance monitoring functions (CMFs). These functions include requirements for constraint modeling notation and for process execution. Second, we discuss how to identify the appropriate approaches to address the open challenges and research directions. Third, we examine the application of the identified functionalities.
A typical approach to quantify compliance uses a count of the number of violations. This can be used as a starting point for designing metrics. However, a continuous scale approach must calculate the “degree” of compliance. Also, the approach must be able to monitor several instances.
Compliance rules may span cases because they require combining data or resources. These instances are monitored at the constraint instance level.