  Advances in Web technology enable personalization proxies that assist users
in satisfying their complex information monitoring and aggregation needs
through the repeated querying of multiple volatile data sources. Such proxies
face a scalability challenge when trying to maximize the number of clients
served while at the same time fully satisfying clients' complex user profiles.
In this work we use an abstraction of complex execution intervals (CEIs)
constructed over simple execution intervals (EIs) represents user profiles and
use existing offline approximation as a baseline for maximizing completeness of
capturing CEIs. We present three heuristic solutions for the online problem of
query scheduling to satisfy complex user profiles. The first only considers
properties of individual EIs while the other two exploit properties of all EIs
in the CEI. We use an extensive set of experiments on real traces and synthetic
data to show that heuristics that exploit knowledge of the CEIs dominate across
multiple parameter settings.
