Author: Morris, J.
Paper Title Page
WEPGF036 Data Categorization And Storage Strategies At RHIC 1
 
  • S. Binello, K.A. Brown, T. D'Ottavio, R.A. Katz, J.S. Laster, J. Morris, J. Piacentino
    BNL, Upton, Long Island, New York, USA
 
  Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
This past year the Controls group within the Collider Accelerator Department at Brookhaven National Laboratory replaced the Network Attached Storage (NAS) system that is used to store software and data critical to the operation of the accelerators. The NAS also serves as the initial repository for all logged data. This purchase was used as an opportunity to categorize the data we store, and review and evaluate our storage strategies. This was done in the context of an existing policy that places no explicit limits on the amount of data that users can log, no limits on the amount of time that the data is retained at its original resolution, and that requires all logged data be available in real-time. This paper will describe how the data was categorized, and the various storage strategies used for each category.
 
poster icon Poster WEPGF036 [0.337 MB]  
 
WEPGF134 Applying Sophisticated Analytics to Accelerator Data at BNLs Collider-Accelerator Complex: Bridging to Repositories, Tools of Choice, and Applications 1
 
  • K.A. Brown, P. Chitnis, T. D'Ottavio, J. Morris, S. Nemesure, S. Perez, D.J. Thomas
    BNL, Upton, Long Island, New York, USA
 
  Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
Analysis of accelerator data has traditionally been done using custom tools, either developed locally or at other laboratories. The actual data repositories are openly available to all users, but it can take significant effort to mine the desired data, especially as the volume of these repositories increases to hundreds of terabytes or more. Much of the data analysis is done in real time when the data is being logged. However, sometimes users wish to apply improved algorithms, look for data correlations, or perform more sophisticated analysis. There is a wide spectrum of desired analytics for this small percentage of the problem domains. In order to address this tools have been built that allow users to efficiently pull data out of the repositories but it is then left up to them to post process that data. In recent years, the use of tools to bridge standard analysis systems, such as Matlab, R, or SciPy, to the controls data repositories, has been investigated. In this paper, the tools used to extract data from the repositories, tools used to bridge the repositories to standard analysis systems, and directions being considered for the future, will be discussed.
 
poster icon Poster WEPGF134 [2.709 MB]