Deep learning for inferring cause of data anomalies

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00106847" target="_blank" >RIV/00216224:14330/18:00106847 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1088/1742-6596/1085/4/042015" target="_blank" >http://dx.doi.org/10.1088/1742-6596/1085/4/042015</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1742-6596/1085/4/042015" target="_blank" >10.1088/1742-6596/1085/4/042015</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep learning for inferring cause of data anomalies

Popis výsledku v původním jazyce

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ’channels’ which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

Název v anglickém jazyce

Deep learning for inferring cause of data anomalies

Popis výsledku anglicky

Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify ’channels’ which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Journal of Physics: Conference Series Volume 1085, Issue 4, 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017

ISBN

—

ISSN

1742-6588

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Physics Publishing

Místo vydání

Seattle

Místo konání akce

Seattle

Datum konání akce

1. 1. 2018

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

000467872200081