# Artificial Intelligence-Based Methods for Business Processes: A Systematic Literature Review
## 5 Discussion  
Our SLR, to answer the RQ \"Which AI-based methods, in the literature, are used to automatize business processes and support the decision-making processes of companies?\" found 387 papers, with about 5.5% of them being relevant to our question. In the research phase, we noticed that, in the filtering process, when applying the exclusion criterion C2 and C4, papers use the term \"method\" sometimes as a synonym for a system, model, or framework; demonstrating the need for greater attention on the distinctions of these concepts, as explained in Section 2.4. Figure 5 demonstrates that the most prevalent subject, present in 95.2% of the publishers, is computer science, and almost 50% approach the mathematics subject, fields related to AI, and methods, respectively. On the other hand, even though the methods are for business processes, less than 10% of publishers are focused on the themes, i.e., business perspective and business, management, and accounting. As evidenced by the keyword co-occurrence network (Figure 6), the most relevant keywords are \"artificial intelligence\", \"business process\", and \"business process model\". These keywords are central and are the mostly connected, which shows the adherence between our search string and the returned papers. These keywords are connected to various others, such as \"decision support systems\", \"process mining\", \"computer science\", and \"process model\". These methods were  
split into six categories: discovery, conformance, support, security, decision support, and enhancement. We noticed that the categories with the most papers were decision support and enhancement. The discovery methods category corresponded to methods that allowed the process discovery for business processes. The AI-based methods presented in this category used ant colony optimization and graph representations to discover a process from an event log and a declarative specification, respectively. The conformance methods category contained techniques that helped reconstruct incomplete traces of a process. The methods from this category used graph and statistical language models to cross-system information and mine text documents to identify missing parts of process traces. The security method category was represented by a work that used a mathematical approach, i.e., triangle inequality and the max-flow min-cut theorem, to measure information leakage for a running process in a workflow. The work identified in the support methods followed a statistical approach by relying on minimum expectations of activities to filter infrequent behavior of event logs prior to process discovery. Decision support methods contained works intended to aid analysts to (re)design or improve business processes, align team goals and identify process complexities. In this sense, the works applied techniques of clustering, i.e., covering arrays, and classification trees, to identify good candidate processes in large repositories, to serve as a base for the design of a new process. Another work used Bayesian networks to suggest business process fragments with the same goal. The Monte Carlo simulation was used to detect process weaknesses and directions for innovation. K-means clustering was employed to group business processes by complexity. Finally, the process enhancement methods had, as its main goal, the improvement of business processes themselves. Some works applied ant colony optimization, artificial bee colony, and matrix factorization to optimize resource allocation in BPs. Others focused on the extension and merge of BPs by using graphs. The last two works proposed using support vector machines and clustering trees to predict the remaining time of ongoing BPs instances. Although we focused on AI methods, one can see that not all papers used an artificial intelligence technique. Figure 10 shows the techniques used by the authors in our systematic review divided into three categories: pure mathematical, computational techniques, and artificial intelligence.