Within the dynamic world of Big Data, traditional systems typically operate in a passive mode, processing and responding to user queries by returning the requested data. However, this methodology falls short of meeting the evolving demands of users who not only wish to analyze data but also to receive proactive updates on topics of interest. To bridge this gap, Big Active Data (BAD) frameworks have been proposed to support extensive data subscriptions and analytics for millions of subscribers. As data volumes and the number of interested users continue to increase, the imperative to optimize BAD systems for enhanced scalability, performance, and efficiency becomes paramount. To this end, this paper introduces three main optimizations, namely: strategic aggregation, intelligent modifications to the query plan, and early result filtering, all aimed at reinforcing a BAD platform's capability to actively manage and efficiently process soaring rates of incoming data and distribute notifications to larger numbers of subscribers.

翻译：在大数据动态发展的背景下，传统系统通常以被动模式运行，通过处理并响应用户查询来返回所需数据。然而，这种方法难以满足用户日益增长的需求，他们不仅希望分析数据，还期望主动获取感兴趣主题的更新。为弥补这一不足，大规模主动数据（BAD）框架被提出，以支持面向数百万订阅者的大规模数据订阅与分析。随着数据量和感兴趣用户数量的持续增长，优化BAD系统以提升可扩展性、性能与效率变得至关重要。为此，本文提出了三项主要优化措施，即：策略性聚合、查询计划的智能修改以及早期结果过滤，这些措施均旨在增强BAD平台主动管理海量涌入数据、高效处理数据流并向更广泛订阅者分发通知的能力。