Fast, just-in-time queries on heterogeneous scientific data
Anastasia Ailamaki - École Polytechnique Fédérale de Lausanne, Switzerland
Session chair: Eleni Chatzi

Abstract : Today’s scientific processes heavily depend on fast and accurate data analysis. Scientists are routinely overwhelmed by the effort needed to manage the volumes of data produced either by observing phenomena or by sophisticated simulations. As data management software is often inefficient, hard to manage, or too generic to serve scientific applications, the scientific community typically uses special-purpose legacy software. With the exponential growth of dataset size and complexity, however, application-specific systems no longer scale to efficiently analyse the relevant parts of their data, thereby slowing down the cycle of analysing, understanding, and preparing new experiments. I will illustrate the different nature of problems we faced when managing brain simulation and patient data for neuroscience applications, and will show how the problems from neuroscience translate into challenges for the data management community. These challenges inspire new technologies which overturn long-stangding assumptions, enable meaningful, timely results and advance scientific discovery. Finally I will describe the challenges associated with gaining access to medical neuroscience data and using it toward advancing our understanding of the functionality of the brain.

https://people.epfl.ch/anastasia.ailamaki