Minisymposium Presentation

The Large Hadron Collider (LHC) is entering an era where the experiments are collecting hundreds of petabytes of data from which a wide variety of science goals can be pursued by distilling the data to high-level observables. Current data analysis methods rely on a combination of experiment-specific centrally-provided code, as well as community supported tools and analysis specific software. In addition, several computing and storage resources are used for processing the data in stages. In the coming years, upgrades to the LHC and the experiments are expecting to result in a major increase in the data volume. In addition, the complexity of the analyses is increasing. This presentation will discuss the current methods used for data analysis in big-science experiments at the LHC, as well as ideas for the future to address challenges in areas such as analysis techniques for parallelized data access, organization of workflows and the incorporation of new technologies such as heterogeneous resources.