10-14 October 2016
San Francisco Marriott Marquis
America/Los_Angeles timezone

Big Data Analytics Tools as Applied to ATLAS Event Data

13 Oct 2016, 15:00
15m
GG A+B (San Francisco Mariott Marquis)

GG A+B

San Francisco Mariott Marquis

Oral Track 5: Software Development Track 5: Software Development

Speaker

Ilija Vukotic (University of Chicago (US))

Description

Big Data technologies have proven to be very useful for storage, processing and visualization of derived
metrics associated with ATLAS distributed computing (ADC) services. Log file data and database records, and
metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for
ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and
resource utilization efficiency charts are produced flexibly through queries against a powerful analytics
cluster. Here we explore whether these techniques and analytics ecosystem can be applied to add new modes
of open, quick, and pervasive access to ATLAS event data so as to simplify access and broaden the reach of
ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver
ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly
simplify usage of big data, statistical and machine learning tools like Spark, Jupyter, R, SciPy,Caffe,
TensorFlow, etc.. Machine learning challenges such as the Higgs Boson Machine Learning Challenge, the
Tracking challenge, Event viewers (VP1, ATLANTIS, ATLASrift), and still to be developed educational and
outreach tools would be able to access the data through a simple REST API. In this preliminary
investigation we focus on derived xAOD data sets. These are much smaller than the primary xAODs having
containers, variables, and events of interest to a particular analysis. Being encouraged with the
performance of Elasticsearch for the ADC analytics platform, we developed an algorithm for indexing derived
xAOD event data. We have made an appropriate document mapping and have imported a full set of standard
model W/Z datasets. We compare the disk space efficiency of this approach to that of standard ROOT files,
the performance in simple cut flow type of data analysis, and will present preliminary results on its
scaling characteristics with different numbers of clients, query complexity, and size of the data
retrieved.

Primary Keyword (Mandatory) Data model
Secondary Keyword (Optional) Data processing workflows and frameworks/pipelines
Tertiary Keyword (Optional) Analysi tools and techniques

Primary author

Ilija Vukotic (University of Chicago (US))

Co-author

Robert William Gardner Jr (University of Chicago (US))

Presentation Materials

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