  The ALICE experiment at CERN LHC is specifically designed for investigating
heavy ion collisions. The upgraded ALICE accommodates a tenfold increase in
PbPb luminosity and a two-order of magnitude surge in minimum bias events. To
address the challenges of high detector occupancy and event pile-ups, advanced
multidimensional data analysis techniques, including machine learning (ML), are
indispensable. Despite ML popularity, the complexity of its models presents
interpretation challenges, and oversimplification in analysis often leads to
inaccuracies.
  Our objective was to develop RootInteractive, a tool for multidimensional
statistical analysis. This tool simplifies data analysis across dimensions,
visualizes functions with uncertainties, and validates assumptions and
approximations. In RootInteractive, it is crucial to easily define the
functional composition of analytical parametric and non-parametric functions,
exploit symmetries, and define multidimensional invariant functions and
corresponding alarms.
  RootInteractive adopts a declarative programming paradigm, ensuring
userfriendliness for experts, students, and educators. It facilitates
interactive visualization, n-dimensional histogramming/projection, and
information extraction on both Python,C++ server and client. Data compression,
datasets with O(10 to 7) entries and O(25) attributes can be interactively
analyzed in a browser with O(0.500-1 GB) size. Representative downsampling and
reweighting/pre-aggregation enable the effective analysis of one year of ALICE
data for various purposes.
