research-article

Homerun: scalable sparse-spectrum reconstruction of aggregated historical data

Authors:

Faisal M. Almutairi,

Christos Faloutsos,

Nicholas Sidiropoulos,

Vladimir ZadorozhnyAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 11, Issue 11

Pages 1496 - 1508

https://doi.org/10.14778/3236187.3236201

Published: 01 July 2018 Publication History

Abstract

Recovering a time sequence of events from multiple aggregated and possibly overlapping reports is a major challenge in historical data fusion. The goal is to reconstruct a higher resolution event sequence from a mixture of lower resolution samples as accurately as possible. For example, we may aim to disaggregate overlapping monthly counts of people infected with measles into weekly counts. In this paper, we propose a novel data disaggregation method, called HomeRun, that exploits an alternative representation of the sequence and finds the spectrum of the target sequence. More specifically, we formulate the problem as so-called basis pursuit using the Discrete Cosine Transform (DCT) as a sparsifying dictionary and impose non-negativity and smoothness constraints. HomeRun utilizes the energy compaction feature of the DCT by finding the sparsest spectral representation of the target sequence that contains the largest (most important) coefficients. We leverage the Alternating Direction Method of Multipliers to solve the resulting optimization problem with scalable and memory efficient steps. Experiments using real epidemiological data show that our method considerably outperforms the state-of-the-art techniques, especially when the DCT of the sequence has a high degree of energy compaction.

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Cited By

Anjum UZadorozhny VKrishnamurthy P(2022)Localization of Unidentified Events with Raw Microblogging DataOnline Social Networks and Media10.1016/j.osnem.2022.10020929(100209)Online publication date: May-2022
https://doi.org/10.1016/j.osnem.2022.100209
Almutairi FKanatsoulis CSidiropoulos N(2021)Prema: Principled Tensor Data Recovery From Multiple Aggregated ViewsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2021.305691815:3(535-549)Online publication date: Apr-2021
https://doi.org/10.1109/JSTSP.2021.3056918
Xu JZadorozhny VGrant J(2020)A-Cure: An accurate information reconstruction from inaccurate data sourcesInformation Systems10.1016/j.is.2020.10150891(101508)Online publication date: Jul-2020
https://doi.org/10.1016/j.is.2020.101508
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Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 11, Issue 11

July 2018

507 pages

ISSN:2150-8097

Editors:
Sihem Amer-Yahia
University of Grenoble Alpes, CNRS
,
Jian Pei
Simon Fraser University

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 July 2018

Published in PVLDB Volume 11, Issue 11

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Cited By

Anjum UZadorozhny VKrishnamurthy P(2022)Localization of Unidentified Events with Raw Microblogging DataOnline Social Networks and Media10.1016/j.osnem.2022.10020929(100209)Online publication date: May-2022
https://doi.org/10.1016/j.osnem.2022.100209
Almutairi FKanatsoulis CSidiropoulos N(2021)Prema: Principled Tensor Data Recovery From Multiple Aggregated ViewsIEEE Journal of Selected Topics in Signal Processing10.1109/JSTSP.2021.305691815:3(535-549)Online publication date: Apr-2021
https://doi.org/10.1109/JSTSP.2021.3056918
Xu JZadorozhny VGrant J(2020)A-Cure: An accurate information reconstruction from inaccurate data sourcesInformation Systems10.1016/j.is.2020.10150891(101508)Online publication date: Jul-2020
https://doi.org/10.1016/j.is.2020.101508
Xu JZadorozhny VGrant J(2020)IncompFuse: a logical framework for historical information fusion with inaccurate data sourcesJournal of Intelligent Information Systems10.1007/s10844-019-00569-654:3(463-481)Online publication date: 1-Jun-2020
https://dl.acm.org/doi/10.1007/s10844-019-00569-6
Yang FAlmutairi FSong HFaloutsos CSidiropoulos NZadorozhny V(2020)TurboLift: fast accuracy lifting for historical data recoveryThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00609-629:5(1129-1148)Online publication date: 9-Mar-2020
https://dl.acm.org/doi/10.1007/s00778-020-00609-6
Almutairi FKanatsoulis CSidiropoulos N(2020)Tendi: Tensor Disaggregation from Multiple Coarse ViewsAdvances in Knowledge Discovery and Data Mining10.1007/978-3-030-47436-2_65(867-880)Online publication date: 11-May-2020
https://dl.acm.org/doi/10.1007/978-3-030-47436-2_65

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