# Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review
## 4 Results and Analysis
### AI-Based Methods--Categories
#### 4.2.6 Process Enhancement Methods  
Resource unavailability or misuse may create bottlenecks in business processes and hurt service level agreements. Managing resource allocations may prevent such issues and optimize resource usage. In this sense, [PERSON] et al. [50] proposed bio-inspired methods to optimize process flow and resource allocation. Their approach started with the business process requirements, regarding tasks, decision gateways, and available resources. Next, it employed bio-agents to provoke changes in the model structure and allocate resources. Finally, after the agent's interaction, the best fit configuration was returned. In [PERSON] et al. [51], the authors improved a method to analyze resource trend usage in business processes. According to the authors, this could be useful for provisioning resources and detecting failures. Their method decomposes numerous data sources into business  
Figure 9: Overall description of decision support systems that empower the analyst decision making process [45; 46].  
process components. The authors concluded that their improved method could identify workload patterns that simultaneously appear in multiple resources and identify increasing, monthly, and changing trends of resource usage in BPs.  
Extending a process is a recurring activity in organizations. It is usually performed when innovating or adjusting process models. [PERSON] et al. [52] created a method for easily extending a business process. It detaches the extension from the code layer of the system and allows the expansion reuse in other contexts. The method proposed by [PERSON] et al. [53] improves the success of the implementation of enterprise resource planning (ERP) systems in companies. Their method constructs parallel flows to assist the main flows in ERPs. Their two main flows are time and quality estimations. The former regards calculating the time duration in each unit of the main flow and aims to prevent delays. The latter captures the conditions in which the current process is passed to the following units so that they can be ready to handle the process properly. In contrast, [PERSON] and [PERSON] [54] focus on a method to merge multiple business processes into a single one. This is relevant in the context of business restructuring or optimization. Their method starts by applying correlated topic modeling to cluster similar processes. Next, a graph mining technique of minimum depth-first (DFS) is applied to find patterns in the set of processes, in terms of sub-graphs. Afterward, tags are created from the sub-graphs, and a string similarity algorithm is employed. Finally, the sub-graphs are merged, and a final business processes is produced.  
Another interesting field tackled by some authors is predicting the remaining path or time in an ongoing business process instance. This may help in contractual procedures, such as service level agreements. It is also important in order to apply countermeasures that can help prevent a process from taking too long or following an undesired path [34]. In this sense, [PERSON] et al. [55] proposed a method for the prediction of performance metrics for on-running process instances. In their method, they first clusterizes processes of the same variant and then attribute to each cluster a predictive model. The model used was predictive clustering trees, and they validated their approach in real-life logs. Moreover, [PERSON] et al. [56] developed a method to predict the remaining path and time for ongoing traces of business processes. Their method considers concept drifts and consists of three distinct phases. Firstly, an annotated transition system is constructed with the aid of fuzzy support vector machines to predict future trace path. Next, the duration of future activities is predicted with support vector regressors. Finally, a concept adaptation method is employed to assign weights to the model's prediction, based on the time interval of the prediction.