This research paper details the development and implementation of efficient techniques for processing multiple, similar aggregate queries in data streaming systems. The authors address the challenges of scaling to handle hundreds of concurrent queries, each with potentially different time windows and selection predicates. Their proposed "on-the-fly" methods avoid computationally expensive static query analysis, offering significant performance improvements (up to an order of magnitude) over existing approaches. The techniques are validated through a performance study using real-world stock market data, demonstrating their practical effectiveness. The core contributions are novel algorithms for shared time slices, shared data fragments, and a combined approach called shared data shards.