TABLE 2.
List of extensions of the CRISP-DM process model framework based on industry-specific requirements (application-specific requirements) due to changing business trends.
| Model | Description | Application |
|---|---|---|
| DMME (Huber et al., 2019) | Adds two new phases between business understanding and data understanding and one phase between model evaluation and deployment | Engineering applications |
| KDDS with big data (Grady, 2016) | Adding various new activities, especially to handle big data | A proposed framework as a need for the current scenario in big data and data science applications |
| CRISP-DM extension for stream analytics (Kalgotra and Sharda, 2016) | Data preparation and data modeling stage to be redefined for multidimensional and time-variant data, where the IoT system sends multiple signals over time | Healthcare application |
| CRISP-DM extension in context of circular economy (Kristoffersen et al., 2019) | Adds a data validation phase and new interactions between different phases | Aligning business analytics with the circular economy goals |
| Context-aware standard process for data mining (CASP-DM) (Martínez-Plumed et al., 2017) | The deployment context of the model can differ from the training context. Therefore, for context-aware ML models and model evaluation, new activities and tasks are added at different phases of CRISP-DM. | Robust and systematic reuse of data transformation and ML models if the context changes |
| APREP-DM (Nagashima and Kato, 2019) | Extended framework for handling outliers, missing data, and data preprocessing at the data-understanding phase | General extension for automated preprocessing of sensor data |
| QM-CRISP-DM (Schäfer et al., 2018) | CRISP-DM extension for the purpose of quality management considering DMIAC cycle | Adding quality management tools in each phase of CRISP-DM framework validated in the real-world electronic production process |
| ASUM-DM (Haffar, 2015) | IBM-SPSS initiative for the practical implementation of the CRISP-DM framework, which combines traditional and agile principals. It implements existing CRSIP-DM phases and adds additional operational, deployment, and project-management phases | General framework that allows comprehensive, scalable, and product-specific implementation |
| A variability-aware design approach for CRISP-DM (Vale Tavares et al., 2018) | Extends the structural framework to capture the variability of data modeling and analysis phase as feature models for more flexible implementation of data process models | General framework which considers model and data variability for the improved automation of data analysis |