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Pregled bibliografske jedinice broj: 1155002

DEBS grand challenge: real-time detection of air quality improvement with Apache Flink


Marić, Josip; Pripužić, Krešimir; Antonić, Martina
DEBS grand challenge: real-time detection of air quality improvement with Apache Flink // DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems
Milano, Italija: The Association for Computing Machinery (ACM), 2021. str. 148-153 doi:10.1145/3465480.3466930 (ostalo, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


CROSBI ID: 1155002 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
DEBS grand challenge: real-time detection of air quality improvement with Apache Flink

Autori
Marić, Josip ; Pripužić, Krešimir ; Antonić, Martina

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems / - : The Association for Computing Machinery (ACM), 2021, 148-153

ISBN
9781450385558

Skup
15th ACM International Conference on Distributed and Event-based Systems

Mjesto i datum
Milano, Italija, 28.06.2021. - 02.07.2021

Vrsta sudjelovanja
Ostalo

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
sensor streams, big data, fast data, geospatial data streams

Sažetak
The topic of the DEBS Grand Challenge 2021 is to develop a solution for detecting areas in which the air quality index (AQI) improved the most when compared to the previous year. The solution must run two given continuous queries in parallel on the incoming sensor data stream which must return the following: 1) a top 50 cities in terms of AQI improvement with their current AQIs and 2) a histogram of the longest streaks of good AQI. The incoming data is accessed through an API which provides streaming sensor measurements in batches. We present our solution based on Apache Flink, a distributed stream processing framework for the cluster. We opted for Flink since its applications can easily be scaled horizontally and vertically by adding computation nodes or increasing available resources, respectively. Flink allows us to divide the given queries into smaller tasks which can be run concurrently on different nodes in order to reduce the overall processing time and thus improve the performance of our solution. In more detail, the following performance intensive tasks are run in parallel on distributed nodes: 1) retrieving measurement batches, 2) assigning a city to each measurement and 3) calculating air quality index per city. We also discuss the main optimizations we have used to improve the performance and present an experimental evaluation of our solution.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekti:
HRZZ-UIP-2017-05-9066 - Učinkovita stvarnovremenska obrada brzih geoprostornih podataka (RETROFIT) (Pripužić, Krešimir, HRZZ ) ( CroRIS)

Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb

Profili:

Avatar Url Martina Antonić (autor)

Avatar Url Krešimir Pripužić (autor)

Poveznice na cjeloviti tekst rada:

doi dl.acm.org

Citiraj ovu publikaciju:

Marić, Josip; Pripužić, Krešimir; Antonić, Martina
DEBS grand challenge: real-time detection of air quality improvement with Apache Flink // DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems
Milano, Italija: The Association for Computing Machinery (ACM), 2021. str. 148-153 doi:10.1145/3465480.3466930 (ostalo, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Marić, J., Pripužić, K. & Antonić, M. (2021) DEBS grand challenge: real-time detection of air quality improvement with Apache Flink. U: DEBS '21: Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems doi:10.1145/3465480.3466930.
@article{article, author = {Mari\'{c}, Josip and Pripu\v{z}i\'{c}, Kre\v{s}imir and Antoni\'{c}, Martina}, year = {2021}, pages = {148-153}, DOI = {10.1145/3465480.3466930}, keywords = {sensor streams, big data, fast data, geospatial data streams}, doi = {10.1145/3465480.3466930}, isbn = {9781450385558}, title = {DEBS grand challenge: real-time detection of air quality improvement with Apache Flink}, keyword = {sensor streams, big data, fast data, geospatial data streams}, publisher = {The Association for Computing Machinery (ACM)}, publisherplace = {Milano, Italija} }
@article{article, author = {Mari\'{c}, Josip and Pripu\v{z}i\'{c}, Kre\v{s}imir and Antoni\'{c}, Martina}, year = {2021}, pages = {148-153}, DOI = {10.1145/3465480.3466930}, keywords = {sensor streams, big data, fast data, geospatial data streams}, doi = {10.1145/3465480.3466930}, isbn = {9781450385558}, title = {DEBS grand challenge: real-time detection of air quality improvement with Apache Flink}, keyword = {sensor streams, big data, fast data, geospatial data streams}, publisher = {The Association for Computing Machinery (ACM)}, publisherplace = {Milano, Italija} }

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