Active learning, a technique in which a learner self-selects the most important unlabeled examples to be labeled by a human expert, is a useful approach when labeled training data is either scarce or expensive to obtain. While active learning has been well-documented in the offline pool-based setting, less attention has been paid to applying active learning in an online streaming setting. In this paper, we introduce a novel generic framework called ART (Availability-aware active leaRning in data sTreams). We examine the multiple-oracle active learning environment and present a novel method for querying multiple imperfect oracles based on dynamic availability schedules. We introduce a flexible availability-based definition of labeling budget for data streams, and present a mechanism to automatically adapt to implicit changes in oracle availability based on past oracle behavior. Compared to the baseline approaches, our results indicate improvements in accuracy and query utility using our availability-based multiple oracle framework.