Improving Sketch Reconstruction Accuracy Using Linear Least Squares Method
Sketch is a sublinear space data structure that allows one to approximately reconstruct the value associated with any given key in an input data stream. It is the basis for answering a number of fundamental queries on data streams, such as range queries, finding quantiles, frequent items, etc. In the networking context, sketch has been applied to identifying heavy hitters and changes, which is critical for traffic monitoring, accounting, and network anomaly detection.
In this paper, we propose a novel approach called lsquare to significantly improve the reconstruction accuracy of the sketch data structure. Given a sketch and a set of keys, we estimate the values associated with these keys by constructing a linear system and finding the optimal solution for the system using linear least squares method. We use a large amount of real Internet traffic data to evaluate lsquare against countmin, the state-of-the-art sketch scheme. Our results suggest that given the same memory requirement, lsquare achieves much better reconstruction accuracy than countmin. Alternatively, given the same reconstruction accuracy, lsquare requires significantly less memory. This clearly demonstrates the effectiveness of our approach.