HBase, MapReduce, and Integrated Data Visualization for Processing Clinical Signal Data

Processing high-density clinical signal data (data from biomedical sensors deployed in the clinical environment) is resource intensive and time consuming. We propose a novel approach to storing and processing clinical signal data based on the Apache HBase distributed column-store and the MapReduce programming paradigm with an integrated web-based data visualization layer. An integrated solution negates the need to marshal data into and out of the storage system while also easily parallelizing the computation, a problem that is becoming more and more important due to increasing numbers of sensors and resulting data. We estimate upwards of 50TB of clinical signal data for a 200-bed medical center within the next 5 years. Consequently, efficient processing of clinical signal data is a vital step towards multivariate analysis of the signal data in order to develop better ways of describing a patient’s clinical status.