In the domain of process mining, each concept typically has a counterpoint or a broader category under which it can be grouped. For each of the specified concepts, I'll propose an opposite concept when applicable and a more general concept that encompasses it:

1. Automated Process Discovery
    - Opposite: Manual Process Modeling - Instead of automatically discovering process models from data, process modeling can be performed manually by experts and analysts who interview stakeholders, conduct workshops, and use their intuition and experience to create process models.
    - More general: Process Mining - Automated process discovery is a subset of process mining, which is the broader field that encompasses the use of specialized algorithms to extract process-related information from event logs.

2. Predictive Monitoring
    - Opposite: Descriptive Analytics - While predictive monitoring attempts to forecast future process states and outcomes, descriptive analytics focuses on analyzing historical data to understand what has happened in the past.
    - More general: Process Analytics - Predictive monitoring is a form of process analytics, a broader discipline that involves analyzing process data to derive insights, including descriptive, diagnostic, predictive, and prescriptive analytics.

3. Bottom-up Discovery
    - Opposite: Top-down Discovery - Bottom-up discovery builds process models by deriving the sequences of activities from the data, whereas top-down discovery starts with a predefined model or hypothesis about the process structure and then validates or adjusts it based on the data observed.
    - More general: Process Model Discovery - Both bottom-up and top-down discoveries are approaches to process model discovery, aiming to create accurate representations of processes.

4. Process Simulation
    - Opposite: Process Analysis without Simulation - While process simulation involves creating a dynamic model of the process that can be used for what-if analyses, there are methods of analyzing processes, such as through static analysis or snapshots, that don't involve simulation.
    - More general: Process Modeling - Simulation is an aspect of process modeling, which encompasses all techniques used to represent, analyze, and improve business processes.

5. Process Efficiency
    - Opposite: Process Inefficiency - This relates to aspects of a process that contribute to waste, delays, or suboptimal performance, highlighting where improvements are required.
    - More general: Process Performance Measurement - Process efficiency is one of the key indicators in the broader field of process performance measurement, which also includes effectiveness, compliance, and other critical factors.

6. Real-time Monitoring
    - Opposite: Post-Hoc Analysis - Post-hoc analysis is concerned with analyzing process data after the fact, as opposed to real-time monitoring, which focuses on tracking the performance and state of a process as it happens.
    - More general: Process Monitoring - Real-time monitoring is a part of process monitoring, which can be performed in real-time or retrospectively to understand, manage, and optimize processes.

Summarily, each concept is part of a larger domain, and its opposite typically reflects a different approach or focus within that domain. The more general concepts are usually superset disciplines that encompass a variety of techniques and methods related to the specific concept.