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Activating big data: Optimizing subscription-driven analytics
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AI
DOI：10.1016/j.is.2026.102767.png)
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Abstract
En 中文
• Traditional Big Data systems fall short of user expectations, lacking support for active updates and enriching incoming data. • The Big Active Data (BAD) framework addresses this gap but can face challenges as data volumes and subscription scales increase. • Three key bottlenecks in BAD frameworks are identified: duplicate processing, overprocessing, and late filtering, along with corresponding optimization techniques to address them. • The introduced optimizations deliver substantial performance improvements, including faster execution, reduced broker overhead, and improved scalability.