Sure! Here are some opposing concepts in process mining, as well as a more general concept:

Opposing Concepts:
1. *Manual Process Mapping*: Instead of automating the discovery of processes with tools or algorithms, manual process mapping involves human analysts manually tracing and documenting workflows. This requires in-depth subject-matter expertise and can be time-consuming but may offer a higher level of precision.

2. *Top-down Design*: Rather than discovering processes through analyzing existing activities and data, top-down design starts with predefined goals or strategies and designs the workflow accordingly. It relies on expert judgment to create process models that fit organizational requirements, which might not always align perfectly with actual practices.

3. *Process Optimization*:
    - **Manual Review**: As opposed to automated efficiency improvements, manual review of processes involves human analysts or consultants examining workflows manually for bottlenecks and inefficiencies. This can lead to deeper insights into the root causes of issues but is often more time-intensive than automated methods.
  - **Outcome-Driven Process Design**: Focusing on desired outcomes rather than process steps can be seen as an alternative. In this approach, organizations design or redesign processes based on their strategic objectives and customer expectations, not just historical data.

4. *Process Visualization*: This concept is the opposite of real-time monitoring  where visual analytics provide insights into past or current performance, whereas process visualization might refer to static or semi-static graphical representations of a process's flow without continuous update. It can serve as an initial reference point before moving towards more dynamic monitoring systems.

5. *Data-Driven Process Improvement*: This concept contrasts with human-driven approaches, emphasizing the use of data for driving decisions about how processes should be improved. While this is already well-entrenched in process mining, a broader context could refer to situations where decisions are made by domain experts or managers without significant input from data and analytics.

General Concept:
* **Process Understanding**: This concept encapsulates a broad spectrum that ranges from the creation of detailed models using tools like process mining up until continuous improvement through feedback loops. It encompasses both the initial discovery and ongoing optimization phases, bridging the gap between understanding the 'as-is' state of processes (including manual mapping or top-down design) to the 'to-be' state (involving automation, real-time monitoring, predictive analytics, etc.). In doing so, it emphasizes an iterative approach towards achieving a more efficient and effective organizational structure.