Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average
Light pollution is a problem that impacts many elements of human life and the environment, including astronomical observations. The authors of this work offer a unique method for detecting anomalies in night sky brightness data recorded using a Sky Quality Meter (SQM). This equipment has been widely...
Published in: | INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS |
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Main Authors: | , , , , , , , , , , |
Format: | Review; Early Access |
Language: | English |
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SPRINGERNATURE
2024
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Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001205418300001 |
author |
Riza Lala Septem; Putra Zulfikar Ali Yunara; Zain Muhammad Iqbal; Trihutama Fajar Zuliansyah; Utama Judhistira Aria; Abu Samah Khyrina Airin Fariza; Herdiwijaya Dhani; Nqz Rinto Anugraha; Mumpuni Emanuel Sungging; Priyatikanto Rhorom |
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spellingShingle |
Riza Lala Septem; Putra Zulfikar Ali Yunara; Zain Muhammad Iqbal; Trihutama Fajar Zuliansyah; Utama Judhistira Aria; Abu Samah Khyrina Airin Fariza; Herdiwijaya Dhani; Nqz Rinto Anugraha; Mumpuni Emanuel Sungging; Priyatikanto Rhorom Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average Computer Science |
author_facet |
Riza Lala Septem; Putra Zulfikar Ali Yunara; Zain Muhammad Iqbal; Trihutama Fajar Zuliansyah; Utama Judhistira Aria; Abu Samah Khyrina Airin Fariza; Herdiwijaya Dhani; Nqz Rinto Anugraha; Mumpuni Emanuel Sungging; Priyatikanto Rhorom |
author_sort |
Riza |
spelling |
Riza, Lala Septem; Putra, Zulfikar Ali Yunara; Zain, Muhammad Iqbal; Trihutama, Fajar Zuliansyah; Utama, Judhistira Aria; Abu Samah, Khyrina Airin Fariza; Herdiwijaya, Dhani; Nqz, Rinto Anugraha; Mumpuni, Emanuel Sungging; Priyatikanto, Rhorom Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS English Review; Early Access Light pollution is a problem that impacts many elements of human life and the environment, including astronomical observations. The authors of this work offer a unique method for detecting anomalies in night sky brightness data recorded using a Sky Quality Meter (SQM). This equipment has been widely utilized in light pollution research worldwide, yielding massive data. However, there is the possibility of experiencing abnormalities or outliers throughout the data collection process due to natural occurrences or measurement errors. This study uses the probabilistic exponential weighted moving average algorithm to find anomalies in SQM data received from Timau Observatory by simulating the streaming procedure on SQM data using Apache Kafka technology. Finally, this study intends to shed fresh knowledge on night sky brightness and light pollution dynamics. The authors could locate and analyze unusual or suspicious phenomena that had previously gone unreported using the anomaly detection approach. These findings can help us better understand light pollution and its environmental and human life effects. Still, they can also help us establish strategies and policies that will reduce light pollution in the future. Furthermore, this work illustrates the potential of anomaly detection as a powerful tool for data analysis in various domains, encouraging the use of this approach in future research. SPRINGERNATURE 2364-415X 2364-4168 2024 10.1007/s41060-024-00535-8 Computer Science WOS:001205418300001 https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001205418300001 |
title |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
title_short |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
title_full |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
title_fullStr |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
title_full_unstemmed |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
title_sort |
Real-time anomaly detection in sky quality meter data using probabilistic exponential weighted moving average |
container_title |
INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS |
language |
English |
format |
Review; Early Access |
description |
Light pollution is a problem that impacts many elements of human life and the environment, including astronomical observations. The authors of this work offer a unique method for detecting anomalies in night sky brightness data recorded using a Sky Quality Meter (SQM). This equipment has been widely utilized in light pollution research worldwide, yielding massive data. However, there is the possibility of experiencing abnormalities or outliers throughout the data collection process due to natural occurrences or measurement errors. This study uses the probabilistic exponential weighted moving average algorithm to find anomalies in SQM data received from Timau Observatory by simulating the streaming procedure on SQM data using Apache Kafka technology. Finally, this study intends to shed fresh knowledge on night sky brightness and light pollution dynamics. The authors could locate and analyze unusual or suspicious phenomena that had previously gone unreported using the anomaly detection approach. These findings can help us better understand light pollution and its environmental and human life effects. Still, they can also help us establish strategies and policies that will reduce light pollution in the future. Furthermore, this work illustrates the potential of anomaly detection as a powerful tool for data analysis in various domains, encouraging the use of this approach in future research. |
publisher |
SPRINGERNATURE |
issn |
2364-415X 2364-4168 |
publishDate |
2024 |
container_volume |
|
container_issue |
|
doi_str_mv |
10.1007/s41060-024-00535-8 |
topic |
Computer Science |
topic_facet |
Computer Science |
accesstype |
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id |
WOS:001205418300001 |
url |
https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001205418300001 |
record_format |
wos |
collection |
Web of Science (WoS) |
_version_ |
1809678907854553088 |