Process mining allows the discovery of business processes from an event log. However, event logs are rapidly increasing in size and process mining algorithms struggle with the computational load when efficient processing is required. This calls for methods that decrease the event log size while still preserving the representativeness of the event log. This paper presents two new algorithms for sampling event logs. The first algorithm called RemainderPlus chooses traces from an event log above a threshold and subsequently selects traces with underrepresented Directly Follows Relations. The second sampling algorithm called AllBehavior selects samples that have a high intersection of Directly Follows Relations with the original event log. Usually, AllBehavior is complemented with RemainderPlus for a more accurate sample representation. They perform well for conformance checking and excel in certain scenarios for process discovery. Thus, both algorithms outperform existing sampling algorithms.