A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after ...

For starters, data modeling can be used in both the pre- and post-processing phases of the data analytics process. As an example, data modeling can be used as a pre-processing technique to extract ...

Training data needs to be homogeneous for robust modeling. Strict training data management is needed throughout the model-building process by controlling and mediating the influence of multiple ...

The Cross-Industry Standard Process for Data Mining, better known as CRISP-DM, has been around for more than a decade, and it’s by far the most widely-used analytics process standard.It’s an ...

1. Prepare the Data. The first step in training an AI model is preparing your data by collecting, cleaning, and preprocessing the information you will use to train the model.

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis ...

One of the most important steps in desiging a database is establishing the data model. Part one of a two-part article describes how to create a logical model.

For example, the Knowledge Discovery Databases model has nine steps, the CRISP-DM model has six steps, and the SEMMA process model has five steps. Applications of Data Mining ...

The Mhaskar group works in two fields: non-linear process control and data-driven modeling and control. Recently his group published work looking at process-aware data-driven modeling and control of a ...