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Mining frequent items in the time fading model

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Massimo Cafaro, Marco Pulimeno, Italo Epicoco, Giovanni Aloisio

Data Structures and Algorithms · Submitted 15 Jan 2016

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms $\lambda$-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

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arXiv ID1601.03892 (version 3)
Pith IDBIZHKZPT, Pith's permanent record of this paper and its review
CitationGiovanni Aloisio, Italo Epicoco, Marco Pulimeno, Massimo Cafaro. "Mining frequent items in the time fading model." arXiv:1601.03892 (2016). https://pith.science/paper/BIZHKZPT
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,560 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.05 · human participants 0.02 · controlled comparison 0.70 · randomized assignment 0.01 · states quantitative result 0.10 · compares with prior results 0.98 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms $\lambda$-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. contribution 1.00 0.93 0.14 asserted 1.00
s1 The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. method 0.94 0.40 0.04 asserted 1.00
s2 It works in the time fading model, mining data streams according to the cash register model. method 0.98 0.50 0.04 asserted 1.00
s3 We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms $\lambda$-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets. result 0.94 0.97 0.85 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.85.

What was examined, and what was not

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  • figures and tables
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  • P abstract
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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present FDCMSS, a new sketch-based algorithm for mining frequent items in data streams. The algorithm cleverly combines key ideas borrowed from forward decay, the Count-Min and the Space Saving algorithms. It works in the time fading model, mining data streams according to the cash register model. We formally prove its correctness and show, through extensive experimental results, that our algorithm outperforms $\lambda$-HCount, a recently developed algorithm, with regard to speed, space used, precision attained and error committed on both synthetic and real datasets.

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