A Toolbox for Realtime Timeseries Anomaly Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10422606" target="_blank" >RIV/00216208:11320/20:10422606 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ICSA-C50368.2020.00053" target="_blank" >https://doi.org/10.1109/ICSA-C50368.2020.00053</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICSA-C50368.2020.00053" target="_blank" >10.1109/ICSA-C50368.2020.00053</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Toolbox for Realtime Timeseries Anomaly Detection

Popis výsledku v původním jazyce

Software architecture practice relies more and more on data-driven decision-making. Data-driven decisions are taken either by humans or by software agents via analyzing streams of timeseries data coming from different running systems. Since the quality of sensed data influences the analysis and subsequent decision-making, detecting data anomalies is an important and necessary part of any data analysis and data intelligence pipeline (such as those typically found in smart and self-adaptive systems). Although a number of data science libraries exist for timeseries anomaly detection, it is both time consuming and hard to plug realtime anomaly detection functionality in existing pipelines. The problem lies with the boilerplate code that needs to be provided for common tasks such as data ingestion, data transformation and preprocessing, invoking of model re-training when needed, and persisting of identified anomalies so that they can be acted upon or further analysed. In response, we created a toolbox for realtime anomaly detection that automates the above common tasks and modularizes the anomaly detection process in a number of clearly defined components. This serves as a plug-in solution for architecting and development of smart systems that have to adapt their behavior at runtime. In this paper, we describe the microservice architecture used by our toolbox and explain how to deploy it for obtaining an out-of-the-box solution for realtime anomaly detection out of ready-to-use components. We also provide an initial assessment of its performance.

Název v anglickém jazyce

A Toolbox for Realtime Timeseries Anomaly Detection

Popis výsledku anglicky

Software architecture practice relies more and more on data-driven decision-making. Data-driven decisions are taken either by humans or by software agents via analyzing streams of timeseries data coming from different running systems. Since the quality of sensed data influences the analysis and subsequent decision-making, detecting data anomalies is an important and necessary part of any data analysis and data intelligence pipeline (such as those typically found in smart and self-adaptive systems). Although a number of data science libraries exist for timeseries anomaly detection, it is both time consuming and hard to plug realtime anomaly detection functionality in existing pipelines. The problem lies with the boilerplate code that needs to be provided for common tasks such as data ingestion, data transformation and preprocessing, invoking of model re-training when needed, and persisting of identified anomalies so that they can be acted upon or further analysed. In response, we created a toolbox for realtime anomaly detection that automates the above common tasks and modularizes the anomaly detection process in a number of clearly defined components. This serves as a plug-in solution for architecting and development of smart systems that have to adapt their behavior at runtime. In this paper, we describe the microservice architecture used by our toolbox and explain how to deploy it for obtaining an out-of-the-box solution for realtime anomaly detection out of ready-to-use components. We also provide an initial assessment of its performance.

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

<a href="/cs/project/GC20-24814J" target="_blank" >GC20-24814J: FluidTrust - popora důvěry pomocí dynamicky proměnlivého řízení přistupu k datům a zdrojům v systémech Průmyslu 4.0</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

2020 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ARCHITECTURE COMPANION (ICSA-C 2020)

ISBN

978-1-72817-415-0

ISSN

—

e-ISSN

—

Počet stran výsledku

4

Strana od-do

278-281

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Salvador, Brazil

Datum konání akce

16. 3. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000587897600046