Event clustering & event series characterization on expected frequency

Conrad M. Albrecht; Marcus Freitag; Theodore G. Van Kessel; Siyuan Lu; Hendrik F. Hamann

doi:10.1109/BigData.2017.8258495

Event clustering & event series characterization on expected frequency

Conrad M. Albrecht
, Marcus Freitag
, Theodore G. Van Kessel
, Siyuan Lu
, Hendrik F. Hamann

Office of the Dean SOMAS

IBM

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT^-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t₁, t₂,..., t_N. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Editors	Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4536-4541
Number of pages	6
ISBN (Electronic)	9781538627143
DOIs	https://doi.org/10.1109/BigData.2017.8258495
State	Published - Jul 1 2017
Event	5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: Dec 11 2017 → Dec 14 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume	2018-January

Conference

Conference	5th IEEE International Conference on Big Data, Big Data 2017
Country/Territory	United States
City	Boston
Period	12/11/17 → 12/14/17

Keywords

Internet of Things
network performance characterization
one-dimensional clustering

Access to Document

10.1109/BigData.2017.8258495

Cite this

Albrecht, C. M., Freitag, M., Van Kessel, T. G., Lu, S., & Hamann, H. F. (2017). Event clustering & event series characterization on expected frequency. In J.-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 4536-4541). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258495

Albrecht, Conrad M. ; Freitag, Marcus ; Van Kessel, Theodore G. et al. / Event clustering & event series characterization on expected frequency. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 4536-4541 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).

@inproceedings{c961a7e4a25f49e5a00f168f4af0deac,

title = "Event clustering \& event series characterization on expected frequency",

abstract = "We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.",

keywords = "Internet of Things, network performance characterization, one-dimensional clustering",

author = "Albrecht, \{Conrad M.\} and Marcus Freitag and \{Van Kessel\}, \{Theodore G.\} and Siyuan Lu and Hamann, \{Hendrik F.\}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE International Conference on Big Data, Big Data 2017 ; Conference date: 11-12-2017 Through 14-12-2017",

year = "2017",

month = jul,

day = "1",

doi = "10.1109/BigData.2017.8258495",

language = "English",

series = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4536--4541",

editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",

booktitle = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",

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Albrecht, CM, Freitag, M, Van Kessel, TG, Lu, S & Hamann, HF 2017, Event clustering & event series characterization on expected frequency. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 4536-4541, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 12/11/17. https://doi.org/10.1109/BigData.2017.8258495

Event clustering & event series characterization on expected frequency. / Albrecht, Conrad M.; Freitag, Marcus; Van Kessel, Theodore G. et al.
Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 4536-4541 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Event clustering & event series characterization on expected frequency

AU - Albrecht, Conrad M.

AU - Freitag, Marcus

AU - Van Kessel, Theodore G.

AU - Lu, Siyuan

AU - Hamann, Hendrik F.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

AB - We present an efficient clustering algorithm applicable to one-dimensional data such as e.g. a series of times-tamps. Given an expected frequency ΔT-1, we introduce an O(N)-efficient method of characterizing N events represented by an ordered series of timestamps t1, t2,..., tN. In practice, the method proves useful to e.g. identify time intervals of missing data or to locate isolated events. Moreover, we define measures to quantify a series of events by varying ΔT to e.g. determine the quality of an Internet of Things service.

KW - Internet of Things

KW - network performance characterization

KW - one-dimensional clustering

UR - https://www.scopus.com/pages/publications/85047738291

U2 - 10.1109/BigData.2017.8258495

DO - 10.1109/BigData.2017.8258495

M3 - Conference contribution

AN - SCOPUS:85047738291

T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

SP - 4536

EP - 4541

BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Big Data, Big Data 2017

Y2 - 11 December 2017 through 14 December 2017

ER -

Albrecht CM, Freitag M, Van Kessel TG, Lu S, Hamann HF. Event clustering & event series characterization on expected frequency. In Nie JY, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 4536-4541. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). doi: 10.1109/BigData.2017.8258495

Event clustering & event series characterization on expected frequency

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Keywords

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