High-accuracy real-time data stream estimations are critical for various applications, and sliding-window-based techniques have attracted wide attention. However, existing solutions struggle to achieve high accuracy, generality, and low memory usage simultaneously. To overcome these limitations, we present MicroscopeSketch, a high-accuracy sketch framework. Our key technique, called adaptive zooming, dynamically adjusts the granularity of counters to maximize accuracy while minimizing memory usage. By applying MicroscopeSketch to three specific tasks---frequency estimation, top-k frequent items discovery, and top-k heavy changes identification-we demonstrate substantial improvements over existing methods, reducing errors by roughly 4 times for frequency estimation and 3 times for identifying top-k items. The relevant source code is available in a GitHub repository.

Multi-set membership queries are fundamental operations in data science. In this paper, we propose a new data structure for multi-set membership queries, named coloring embedder, which is fast, accurate, and memory efficient. The idea of coloring embedder is to first map elements to a high-dimensional space, which nearly eliminates hashing collisions, and then use a dimensional reduction representation, similar to coloring a graph, to save memory. Theoretical proofs and experimental results show that the coloring embedder is effective in solving the problem of multi-set membership queries. We also find that web cache sharing is one of the typical application scenarios of the multi-set membership queries and current methods based on Bloom filters always send redundant queries. We apply coloring embedder to web cache sharing by arranging our data structure on the on-chip and off-chip memory and designing query, insertion and deletion operations for this scenario. The experimental results show that compared with the present method, our method can reduce the queries sent by proxies while reaching equal hit rate with the same size of on-chip memory. The source code of coloring embedder has been released on Github.

Finding top-k items in data streams is a fundamental problem in data mining. Existing algorithms that can achieve unbiased estimation suffer from poor accuracy. In this paper, we propose a new sketch, WavingSketch, which is much more accurate than existing unbiased algorithms. WavingSketch is generic, and we show how it can be applied to four applications: finding top-k frequent items, finding top-k heavy changes, finding top-k persistent items, and finding top-k Super-Spreaders. We theoretically prove that WavingSketch can provide unbiased estimation, and then give an error bound of our algorithm. Our experimental results show that, compared with the state-of-the-art, WavingSketch has 4.50 times higher insertion speed and up to 9 x 106 times (2 x 104 times in average) lower error rate in finding frequent items when memory size is tight. For other applications, WavingSketch can also achieve up to 286 times lower error rate. All related codes are open-sourced and available at Github anonymously.