Forest of Normalized Trees: Fast and Accurate Density Estimation of Streaming Data

Rehn, Patrick; Ahmadi, Zahra; Krämer, Stefan

doi:10.1109/dsaa.2018.00030

Cited by 2 publications

(1 citation statement)

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“…In several modern application domains (e.g., social networks, online finance, online transaction systems), data are generated in a continuous fashion, and at such a high rate that their processing requires on-the-fly computation which can afford to maintain only a small portion of the data in memory. This computational scenario is captured by the well-known streaming model, which has received everincreasing attention in the literature over the last two decades [26,30,31,35,38]. In some prominent applications, it is also important that older data in the stream (i.e., those outside a sliding window containing the N most recent data items) be considered "stale" and thus be disregarded in the computation.…”

Section: Introductionmentioning

confidence: 99%

Adaptive k-center and diameter estimation in sliding windows

Pellizzoni

Pietracaprina

Pucci

2022

Int J Data Sci Anal

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In this paper we present novel streaming algorithms for the k-center and the diameter estimation problems for general metric spaces under the sliding window model. The key idea behind our algorithms is to maintain a small coreset which, at any time, allows to compute a solution to the problem under consideration for the current window, whose quality can be made arbitrarily close to the one of the best solution attainable by running a polynomial-time sequential algorithm on the entire window. Remarkably, the size of our coresets is independent of the window length and can be upper bounded by a function of the target number of centers (for the k-center problem), of the desired accuracy, and of the characteristics of the current window, namely its doubling dimension and aspect ratio. One of the major strengths of our algorithms is that they adapt obliviously to these two latter characteristics. We also provide experimental evidence of the practical viability of the algorithms and their superiority over the current state of the art.

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