ICALEPCS2019 - List of Authors (D’Ottavio, T.)

Paper	Title	Page
MOPHA043	Accelerator Control Data Mining with WEKA	293
	W. Fu, K.A. Brown, T. D’Ottavio, P.S. Dyer, S. Nemesure BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. Accelerator control systems generates and stores many time-series data related to the performance of an accelerator and its support systems. Many of these time series data have detectable change trends and patterns. Being able to timely detect and recognize these data change trends and patterns, analyse and predict the future data changes can provide intelligent ways to improve the controls system with proactive feedback/forward actions. With the help of advanced data mining and machine learning technology, these types of analyses become easier to produce. As machine learning technology matures with the inclusion of powerful model algorithms, data processing tools, and visualization libraries in different programming languages (e.g. Python, R, Java, etc), it becomes relatively easy for developers to learn and apply machine learning technology to online accelerator control system data. This paper explores time series data analysis and forecasting in the Relativistic Heavy Ion Collider (RHIC) control systems with the Waikato Environment for Knowledge Analysis (WEKA) system and its Java data mining APIs.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA043
About •	paper received ※ 20 September 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THCPL07	Experience Using NuPIC to Detect Anomalies in Controls Data	1612
	T. D’Ottavio, P.S. Dyer, J. Piacentino, M.R. Tomko BNL, Upton, New York, USA
	NuPIC (Numenta Platform for Intelligent Computing) is an open-source computing platform that attempts to mimic neurological pathways in the human brain. We have used the Python implementation to explore the utility of using this system to detect anomalies in both stored and real-time data coming from the controls system for the RHIC Collider at Brookhaven National Laboratory. This paper explores various aspects of that work including the types of data most suited to anomaly detection, the likelihood of developing false positive and negative anomaly results, and experiences with training the system. We also report on the use of this software for monitoring various parts of the controls system in real-time.
	Slides THCPL07 [11.115 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL07
About •	paper received ※ 02 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Accelerator Control Data Mining with WEKA

293

W. Fu, K.A. Brown, T. D’Ottavio, P.S. Dyer, S. Nemesure
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
Accelerator control systems generates and stores many time-series data related to the performance of an accelerator and its support systems. Many of these time series data have detectable change trends and patterns. Being able to timely detect and recognize these data change trends and patterns, analyse and predict the future data changes can provide intelligent ways to improve the controls system with proactive feedback/forward actions. With the help of advanced data mining and machine learning technology, these types of analyses become easier to produce. As machine learning technology matures with the inclusion of powerful model algorithms, data processing tools, and visualization libraries in different programming languages (e.g. Python, R, Java, etc), it becomes relatively easy for developers to learn and apply machine learning technology to online accelerator control system data. This paper explores time series data analysis and forecasting in the Relativistic Heavy Ion Collider (RHIC) control systems with the Waikato Environment for Knowledge Analysis (WEKA) system and its Java data mining APIs.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-MOPHA043

About •

paper received ※ 20 September 2019 paper accepted ※ 08 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THCPL07

Experience Using NuPIC to Detect Anomalies in Controls Data

1612

T. D’Ottavio, P.S. Dyer, J. Piacentino, M.R. Tomko
BNL, Upton, New York, USA

NuPIC (Numenta Platform for Intelligent Computing) is an open-source computing platform that attempts to mimic neurological pathways in the human brain. We have used the Python implementation to explore the utility of using this system to detect anomalies in both stored and real-time data coming from the controls system for the RHIC Collider at Brookhaven National Laboratory. This paper explores various aspects of that work including the types of data most suited to anomaly detection, the likelihood of developing false positive and negative anomaly results, and experiences with training the system. We also report on the use of this software for monitoring various parts of the controls system in real-time.

Slides THCPL07 [11.115 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2019-THCPL07

About •

paper received ※ 02 October 2019 paper accepted ※ 09 October 2019 issue date ※ 30 August 2020

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)