Data Tech

In some fields, big data doesn't just mean lots of rows — it also means lots of variables. For example, in the fields of proteomics and genomics, the analysis of gene expression data or mass spectrometry readings is increasingly important for diagnosing diseases. But there can be hundreds of thousands of SNPs from thousands of patients in a single study. So how do you build predictive models in which the number of independent variables can number in the millions?

In this demonstration we show how one manufacturer used a series of 'wide data' innovations in the TIBCO Data Science platform to build a digital twin model that identified causes of yield loss in a semiconductor fab. The result represents the successful convergence of IoT, big data, and machine learning to build a predictive model on over 6 million features.