Todays complex software systems often are systems of systems (SoS), comprising many heterogeneous architectural elements. Since the full behavior of the SoS only arises at runtime, monitoring for checking their conformance to requirements is essential. For this purpose, ReMinds, a flexible runtime monitoring framework, has been developed. It relies on constraints defining the desired behavior of the SoS. These constraints are used to check the SoS by analyzing events as well as their properties and checking whether they violate the defined behavior. For example, constraints can be used to check the expected occurrence, timing and order of events, including properties such as data ranges, at runtime. Constraints in ReMinds are written using a constraint domain-specific language (DSL). To remit domain experts the necessity of writing all these constraints manually, mining algorithms were introduced as part of an (ongoing) PhD thesis. Based on analyzing event patterns and data contained in stored system runs (event logs), constraints can be automatically mined by these algorithms. In the scope of mining, this master thesis is about a tool allowing users to interact with those algorithms, i.e., select among multiple algorithms and configure them based on the available event logs and experiment with the results produced by the algorithms, e.g., filter and rank mined constraint candidates in different ways. Eventually, the tool allows to transfer constraints selected by the user to the ReMinds Monitoring Server, where they can be used to check systems at runtime. The tool is, as the mining algorithms, designed in a fashion allowing to be used for many domains without adaptation. This thesis also describes the development of an algorithm enabling classification of similar constraints into groups. It allows users to handle large amounts of constraints. We evaluated the tool based on feedback from industry users collected in a workshop.