Authors:
David Kernert
1
;
Norman May
1
;
Michael Hladik
1
;
Klaus Werner
2
and
Wolfgang Lehner
3
Affiliations:
1
SAP SE, Germany
;
2
Université de Nantes, France
;
3
Technische Universitaet Dresden, Germany
Keyword(s):
In-memory Databases, Scientific Applications.
Related
Ontology
Subjects/Areas/Topics:
Architectural Concepts
;
Big Data
;
Business Analytics
;
Business and Social Applications
;
Data Engineering
;
Data Management and Quality
;
Databases and Data Security
;
e-Business
;
Enterprise Information Systems
;
Query Processing and Optimization
;
Statistics Exploratory Data Analysis
;
Workflow Management and Databases
Abstract:
In order to confirm their theoretical assumptions, physicists employ Monte-Carlo generators to produce millions
of simulated particle collision events and compare them with the results of the detector experiments. The
traditional, static analysis workflow of physicists involves creating and compiling a C++ program for each
study, and loading large data files for every run of their program. To make this process more interactive and
agile, we created an application that loads the data into the relational in-memory column store DBMS SAP
HANA, exposes raw particle data as database views and offers an interactive web interface to explore this data.
We expressed common particle physics analysis algorithms using SQL queries to benefit from the inherent
scalability and parallelization of the DBMS. In this paper we compare the two approaches, i.e. manual analysis
with C++ programs and interactive analysis with SAP HANA. We demonstrate the tuning of the physical
database schema and the SQL que
ries used for the application. Moreover, we show the web-based interface that
allows for interactive analysis of the simulation data generated by the EPOS Monte-Carlo generator, which is
developed in conjunction with the ALICE experiment at the Large Hadron Collider (LHC), CERN.
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