## 2 Related Work  
The work presented in this paper is related to initiatives and projects in the Business Process Model Abstraction (BPMA) and Business Process Variability Modeling (BPMV) domains. Main similarities and differences regarding some of the most significant proposals in these areas are reviewed. BPMA has been proposed in last years (see [8; 9; 10; 11]) as a solution to a common problem appearing in large companies worldwide. Traditionally, companies maintain process model repositories including thousands of different workflows for each of the perspectives and goals to be tackled. As a consequence, the models within their repositories tend to include complex interrelationships, and they may even include overlaps or the same behaviour in different perspectives [8]. In this scenario, the primary purpose of BPMA is to design detailed models containing the whole set of information about the processes and to provide suitable abstraction mechanisms for generating the desired model view. This way, different kinds of users can access different views and perspectives on processes with an  
Figure 1: Input and output of the simplification process. adapted visualisation. The approach proposed in our work can help to achieve these objectives, as it is intended to provide an algorithm to generate a simplification on a process model based on a set of relevant activities. According to [8], and based on the cartographic generalisation by [12], BPMA deals with three main generalisation components: \"why\", \"when\" and \"how\". \"Why\" is concerned with the reasons and the goals of the abstraction. \"When\" includes the rules, thresholds and conditions to perform the abstraction. \"How\" describes the transformation and techniques for achieving the abstraction. This last one explains the method and the algorithms for performing the simplifications of the models. The simplification algorithm proposed is framed on the \"how\" generalisation component of BPMA. The basic abstraction operations defined in BPMA are \"elimination\" (to delete elements in the model to reduce the coverage level) and \"aggregation\" (to create groups of elements to increase the granularity level). The former produces a model containing no information about the deleted elements while the latter preserves some part of the information. In the present work, the simplification algorithm is concerned with the elimination operation using a pattern matching method. In fact, some BPMA approaches, such as [13; 14; 15], perform eliminations using a sibling concept including Event-Driven Process Chains (EPCs) [16] fragments and transformation rules. There are also some other works that explore the pattern reduction method, using a closer approach to this proposal. In [17], five reduction rules that remove the undesired elements in the workflow model are presented. The authors aim to identify structural conflicts in the process model (like deadlocks and lack of synchronisation). They propose some correctness criteria to identify this structural conflicts by exploring all possible instance sub-graphs. However, starting with a brute force method to explore them may not be computationally effective in many contexts. To fight back this issue, they propose to perform firstly a simplification of the model based on these five rules previously identified. In our case, the objective is to delete from the model all the elements and structures that are correct but not significant for purposes of analysis, and, then, to explore the resulting model. However, even the simplification concept can be similar in some way to the previous works, the final goal is very different. The major similarities are related to the \"adjacent reduction\" and \"closed reduction\" rules. These rules can be applied in both works: [17] and the current one. In any case, these works tackle the issue exclusively from a theoretical point of view, not a practical one. They do not provide a practical mechanism capable of carrying out and implementing these operations automatically.