Digital information flows impact all human experiences. The proliferation of large, heterogeneous, and spatio-temporal data requires novel approaches for managing, modeling, analyzing, interpreting, and visualizing complex information. The scientific community is developing, validating, productizing, and supporting novel mathematical techniques, advanced statistical computing algorithms, transdisciplinary tools, and effective artificial intelligence apps.

Spacekime analytics is a new technique for modeling high-dimensional longitudinal data. This approach relies on extending the notions of time, events, particles, and wavefunctions to complex-time (kime), complex-events (kevents), data, and inference-functions. We will illustrate how the kime-magnitude (longitudinal time order) and kime-direction (phase) affect the subsequent predictive analytics and the induced scientific inference. The mathematical foundation of spacekime calculus reveals interesting statistical implications including inferential uncertainty and a Bayesian formulation of spacekime analytics. Complexifying time allows the lifting of all commonly observed processes (e.g., time-series) from the classical 4D Minkowski spacetime to a 5D spacekime manifold (e.g., kime-surfaces), where a number of mathematical problems remain to be solved.