We formulate and study a new computational model for dynamic data. In this model, the data changes gradually in time and the computation has access to only a small part of the data in each step. The goal is to design algorithms that output solutions to computational problems on the data at any given time. As the data is constantly changing and the algorithm may not be unaware of these changes, it cannot be expected to always output the exact right
solution; we are interested in algorithms that guarantee good approximate solutions.

We study fundamental computation problems, including sorting and selection, where the true ordering of the elements changes in time and the algorithm can only probe in each step
the order of a few pairs; and connectivity and minimum spanning trees in graphs where edges` existence and weight change over time and the algorithm can only track these changes by probing a few vertex or edges per step. This framework captures the inherent trade off between the complexity of maintaining an up-to-date view of the data and the quality of results computed with the available view.

(Join work with Aris Anagnostopoulos, Ravi Kumarb, Mohammad Mahdianb and Fabio Vandin)