Anomaly detection over streaming data: Indy500 case study

Chathura Widanage; Jiayu Li; Sahil Tyagi; Ravi Teja; Bo Peng; Supun Kamburugamuve; Dan Baum; Dayle Smith; Judy Qiu; Jon Koskey

doi:10.1109/CLOUD.2019.00015

Anomaly detection over streaming data: Indy500 case study

Chathura Widanage
, Jiayu Li
, Sahil Tyagi
, Ravi Teja
, Bo Peng
, Supun Kamburugamuve
, Dan Baum
, Dayle Smith
, Judy Qiu
, Jon Koskey

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

13 Scopus citations

Abstract

Sports racing is attracting billions of audiences each year. It is powered and transformed by the latest data analysis technologies, from race car design, driving skill improvements to audience engagement on social media. However, most of the data processing are off-line and retrospective analysis. The emerging real-time data analysis from the Internet of Things (IoT) result in fast data streams generated from distributed sensors. Applying advanced Machine Learning/Artificial Intelligence over such data streams to discover new information, predict future insights and make control decision is a crucial process. In this paper, we start by articulating racing car big data characteristics and present time-critical anomaly detection of the racing cars with the real-time sensors of cars and the tracks from actual racing events. We build a scalable system infrastructure based on neuro-morphic Hierarchical Temporal Memory Algorithm (HTM) algorithm and Storm stream processing engine. By courtesy of historical Indy500 racing logs, evaluation experiments on this prototype system demonstrate good performance in terms of anomaly detection accuracy and service level objective (SLO) of latency for a real-world streaming application.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services
Editors	Elisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Michael Goul, Katsunori Oyama
Publisher	IEEE Computer Society
Pages	9-16
Number of pages	8
ISBN (Electronic)	9781728127057
DOIs	https://doi.org/10.1109/CLOUD.2019.00015
State	Published - Jul 2019
Externally published	Yes
Event	12th IEEE International Conference on Cloud Computing, CLOUD 2019 - Milan, Italy Duration: Jul 8 2019 → Jul 13 2019

Publication series

Name	IEEE International Conference on Cloud Computing, CLOUD
Volume	2019-July
ISSN (Print)	2159-6182
ISSN (Electronic)	2159-6190

Conference

Conference	12th IEEE International Conference on Cloud Computing, CLOUD 2019
Country/Territory	Italy
City	Milan
Period	07/8/19 → 07/13/19

Funding

ACKNOWLEDGMENT We gratefully acknowledge support from the Intel Parallel Computing Center (IPCC) grant, NSF CIF-DIBBS 143054, EEC 1720625 and IIS 1838083 Grants. We appreciate the support from IU PHI, FutureSystems team and ISE Modelling and Simulation Lab. We gratefully acknowledge support from the Intel Parallel Computing Center (IPCC) grant, NSF CIF-DIBBS 143054, EEC 1720625 and IIS 1838083 Grants. We appreciate the support from IU PHI, FutureSystems team and ISE Modelling and Simulation Lab.

Keywords

Anomaly detection
Big data
Edge computing
Neuro morphic computing
Stream processing

Access to Document

10.1109/CLOUD.2019.00015

Cite this

Widanage, C., Li, J., Tyagi, S., Teja, R., Peng, B., Kamburugamuve, S., Baum, D., Smith, D., Qiu, J., & Koskey, J. (2019). Anomaly detection over streaming data: Indy500 case study. In E. Bertino, C. K. Chang, P. Chen, E. Damiani, M. Goul, & K. Oyama (Eds.), Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services (pp. 9-16). Article 8814502 (IEEE International Conference on Cloud Computing, CLOUD; Vol. 2019-July). IEEE Computer Society. https://doi.org/10.1109/CLOUD.2019.00015

Widanage, Chathura ; Li, Jiayu ; Tyagi, Sahil et al. / Anomaly detection over streaming data : Indy500 case study. Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. editor / Elisa Bertino ; Carl K. Chang ; Peter Chen ; Ernesto Damiani ; Michael Goul ; Katsunori Oyama. IEEE Computer Society, 2019. pp. 9-16 (IEEE International Conference on Cloud Computing, CLOUD).

@inproceedings{30b47c29e5214dd9b4ef5319be09759e,

title = "Anomaly detection over streaming data: Indy500 case study",

abstract = "Sports racing is attracting billions of audiences each year. It is powered and transformed by the latest data analysis technologies, from race car design, driving skill improvements to audience engagement on social media. However, most of the data processing are off-line and retrospective analysis. The emerging real-time data analysis from the Internet of Things (IoT) result in fast data streams generated from distributed sensors. Applying advanced Machine Learning/Artificial Intelligence over such data streams to discover new information, predict future insights and make control decision is a crucial process. In this paper, we start by articulating racing car big data characteristics and present time-critical anomaly detection of the racing cars with the real-time sensors of cars and the tracks from actual racing events. We build a scalable system infrastructure based on neuro-morphic Hierarchical Temporal Memory Algorithm (HTM) algorithm and Storm stream processing engine. By courtesy of historical Indy500 racing logs, evaluation experiments on this prototype system demonstrate good performance in terms of anomaly detection accuracy and service level objective (SLO) of latency for a real-world streaming application.",

keywords = "Anomaly detection, Big data, Edge computing, Neuro morphic computing, Stream processing",

author = "Chathura Widanage and Jiayu Li and Sahil Tyagi and Ravi Teja and Bo Peng and Supun Kamburugamuve and Dan Baum and Dayle Smith and Judy Qiu and Jon Koskey",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 12th IEEE International Conference on Cloud Computing, CLOUD 2019 ; Conference date: 08-07-2019 Through 13-07-2019",

year = "2019",

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Widanage, C, Li, J, Tyagi, S, Teja, R, Peng, B, Kamburugamuve, S, Baum, D, Smith, D, Qiu, J & Koskey, J 2019, Anomaly detection over streaming data: Indy500 case study. in E Bertino, CK Chang, P Chen, E Damiani, M Goul & K Oyama (eds), Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services., 8814502, IEEE International Conference on Cloud Computing, CLOUD, vol. 2019-July, IEEE Computer Society, pp. 9-16, 12th IEEE International Conference on Cloud Computing, CLOUD 2019, Milan, Italy, 07/8/19. https://doi.org/10.1109/CLOUD.2019.00015

Anomaly detection over streaming data: Indy500 case study. / Widanage, Chathura; Li, Jiayu; Tyagi, Sahil et al.
Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. ed. / Elisa Bertino; Carl K. Chang; Peter Chen; Ernesto Damiani; Michael Goul; Katsunori Oyama. IEEE Computer Society, 2019. p. 9-16 8814502 (IEEE International Conference on Cloud Computing, CLOUD; Vol. 2019-July).

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

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Widanage C, Li J, Tyagi S, Teja R, Peng B, Kamburugamuve S et al. Anomaly detection over streaming data: Indy500 case study. In Bertino E, Chang CK, Chen P, Damiani E, Goul M, Oyama K, editors, Proceedings - 2019 IEEE International Conference on Cloud Computing, CLOUD 2019 - Part of the 2019 IEEE World Congress on Services. IEEE Computer Society. 2019. p. 9-16. 8814502. (IEEE International Conference on Cloud Computing, CLOUD). doi: 10.1109/CLOUD.2019.00015

Anomaly detection over streaming data: Indy500 case study

Abstract

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Keywords

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