Process cubing involves utilizing cube structures for data analysis purposes within complex process mining contexts, typically found in business intelligence applications using event data warehouse systems such as SAP Event Mesh Services (E2B) or similar platforms.

The role of Process Cubes:

Multi-dimensional process cubes provide advanced analytics capabilities by aggregating vast amounts of raw logs extracted from process execution traces into structured forms allowing for real-time analysis and visualization purposes.

Process Cubing differs in the following aspects compared to traditional process mining approaches :

1. **Aggregation and Structure**: Traditional process mining typically involves simple data aggregation methods focusing on sequences, timing relationships between activities within a process instance, or causal relationships among events. Meanwhile cubes utilize advanced SQL-based cube structure which can handle multidimensionality enabling complex analytics operations.

2\. **Complexity Handling**: Traditional methods may struggle with handling complexity coming from massive event logs due to the high dimensionality of data involved. Process Cubes leverage structured datasets containing pre-aggregated and normalized process traces for in-depth analysis purposes.

3\. **Real-Time Analysis**: Traditional process mining involves time-consuming processes such as extracting raw data, transforming them into useful information, then analyzing them manually or through dedicated software tools like BPM Suite Performance Monitor Tool (BPMST), Atrium Analytics from IBM iBP, etc.). Cubes enable real-time analysis by having pre-aggregated and structured data ready for querying purposes.

4\. **Visualization**: Traditional process mining outputs rely heavily on manual interpretation of data visualizations provided by specific tools designed mainly around BPMN diagrams or custom dashboards created manually. Process cube visualization interfaces provide interactive graphical views based on complex analytics performed behind-the-scenes through the cube's structure allowing users to drill down deeper insights quickly.

5\. **Scalability**: Traditional process mining methods may struggle with scalability due to the rapid increase in volume and complexity of logged events generated by modern applications running at high throughput rates requiring significant manual effort for processing purposes across different instances simultaneously. Process cubes allow scalable analysis since they handle large datasets efficiently using distributed architectures supporting parallel computing environments.

Overall, process cubes serve as an essential component for advanced analytics within process mining domains offering advantages beyond mere data extraction and aggregation but also providing means for deeper insights discovery through structured querying interfaces designed specifically around the cube structure concept itself enhancing visualization capabilities alongside high performance in real-time analysis scenarios compared to traditional approaches limited by static views derived manually over raw logs.

Please note that this explanation simplifies concepts significantly aiming towards understanding purposes without delving deeply into technicalities specific implementations might require based on different platforms or environments utilized commercially nowadays.

Let me know if you would like any clarification further explained or additional context provided regarding process cube implementation details within multi-dimensional process mining applications.