  Nowadays, many web databases "hidden" behind their restrictive search
interfaces (e.g., Amazon, eBay) contain rich and valuable information that is
of significant interests to various third parties. Recent studies have
demonstrated the possibility of estimating/tracking certain aggregate queries
over dynamic hidden web databases. Nonetheless, tracking all possible aggregate
query answers to report interesting findings (i.e., exceptions), while still
adhering to the stringent query-count limitations enforced by many hidden web
databases providers, is very challenging. In this paper, we develop a novel
technique for tracking and discovering exceptions (in terms of sudden changes
of aggregates) over dynamic hidden web databases. Extensive real-world
experiments demonstrate the superiority of our proposed algorithms over
baseline solutions.
