The list provided focuses on the automation of process discovery using AI or machine learning. The opposite concept is "Manual and hands-on process mining," which involves human input during the entire process. This approach allows for deeper understanding of processes, decision-making capabilities, and adaptability in response to changing business requirements.

A more general concept related to process mining would be "Process Mining as a Service" or "PaaS." This model introduces a service-oriented architecture where users can obtain pre-configured or customizable services for process mining. It offers a modular approach that enables users to integrate various AI and machine learning algorithms, allowing for scalability and flexibility in handling large-scale business processes.

The explanation of the proposed concepts:

1. **Automated Process Discovery:**
   - This concept implies using technology to automate the discovery process of process models from data sources.
   - It emphasizes the use of software or algorithms that automatically extract useful information from various data formats, such as transaction logs, and create models that represent business processes.

2. **Predictive Monitoring:**
   - This approach uses AI techniques for real-time monitoring and analysis to predict potential issues within a process before they become significant problems.
   - It often involves the use of predictive analytics algorithms that can anticipate future events or deviations from expected behavior.

3. **Bottom-up Discovery:**
   - This concept refers to the process of discovering processes by focusing on individual steps in a process rather than analyzing entire workflows systematically.
   - It emphasizes a detailed exploration of specific activities, components, and interactions within the process model without necessarily tracing back through the full system chain or hierarchy.

4. **Process Simulation:**
   - This method involves creating simulations to understand how a business process would behave under different scenarios or with varying conditions.
   - The goal is to assess process effectiveness and identify areas for improvement by replicating real-world conditions in a controlled environment.

5. **Process Efficiency:**
   - This concept focuses on identifying and enhancing processes through efficiency improvements, aiming to increase the overall performance of business operations.
   - Techniques for optimization may include streamlining workflows, reducing waste, or optimizing resource allocation to ensure maximum output with minimum effort.

6. **Real-time Monitoring:**
   - This concept involves monitoring real-time data for changes in process performance or other indicators relevant to processes being monitored.
   - Real-time capabilities are crucial in dynamic and highly changing business environments where insights can be crucial for swift decision-making and adapting processes accordingly.

In summary, the proposed concepts aim to cover different aspects of process mining, ranging from automated discovery tools to more human-in-the-loop approaches. The concept "Process Mining as a Service" bridges this gap by providing pre-configured services that leverage AI and machine learning, thus integrating automation with manual effort while maintaining scalability and adaptability.