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Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices

Autor: Das, Roshan Bharath, Makkes, Marc X., Uta, Alexandru, Wang, Lin, Bal, Henri, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny

Publikováno v: 2019 IEEE International Conference on Big Data (Big Data): [Proceedings], 441-448
STARTPAGE=441;ENDPAGE=448;TITLE=2019 IEEE International Conference on Big Data (Big Data)
IEEE BigData
Das, R B, Makkes, M X, Uta, A, Wang, L & Bal, H 2020, Aves : A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices . in C Baru, J Huan, L Khan, X T Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & Y F Ye (eds), 2019 IEEE International Conference on Big Data (Big Data) : [Proceedings] ., 9005607, Institute of Electrical and Electronics Engineers Inc., pp. 441-448, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 9/12/19 . https://doi.org/10.1109/BigData47090.2019.9005607

Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simp

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::659d21582d631572f524bb8703b0a95b
https://research.vu.nl/en/publications/5db51c15-6a70-47d9-951f-79c9c2137a60

A Programming Framework for Heterogeneous Stream Analytics

Autor: Das, Roshan Bharath, Makkes, Marc X., Uta, Alexandru, Wang, Lin, Bal, Henri, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny

Publikováno v: IEEE BigData
2019 IEEE International Conference on Big Data (Big Data 2019): [Proceedings], 6030-6032
STARTPAGE=6030;ENDPAGE=6032;TITLE=2019 IEEE International Conference on Big Data (Big Data 2019)
Das, R B, Makkes, M X, Uta, A, Wang, L & Bal, H 2020, A Programming Framework for Heterogeneous Stream Analytics . in C Baru, J Huan, L Khan, X T Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & Y F Ye (eds), 2019 IEEE International Conference on Big Data (Big Data 2019) : [Proceedings] ., 9006113, Institute of Electrical and Electronics Engineers Inc., pp. 6030-6032, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 9/12/19 . https://doi.org/10.1109/BigData47090.2019.9006113

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad52b01938cc8fca42675dfdd9f3efdc
https://doi.org/10.1109/bigdata47090.2019.9006113

An innovative online process mining framework for supporting incremental GDPR compliance of business processes

Autor: Zaman, Rashid, Cuzzocrea, Alfredo, Hassani, Marwan, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny

Publikováno v: 2019 IEEE International Conference on Big Data, Big Data 2019, 2982-2991
STARTPAGE=2982;ENDPAGE=2991;TITLE=2019 IEEE International Conference on Big Data, Big Data 2019
2019 IEEE International Conference on Big Data (Big Data)
IEEE BigData

GDPR (General Data Protection Regulation) is a new regulation of the European Union that superimposes strict privacy constraints on storing, accessing and processing user data, as a way to ensure that personal user data are not violated neither discl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be6f7bd764d28909a18d3a6607871119
http://www.scopus.com/inward/record.url?scp=85081340678&partnerID=8YFLogxK

Understanding Data Similarity in Large-Scale Scientific Datasets

Autor: Alina Lazar, Deb Agarwal, Lavanya Ramakrishnan, Ludovico Bianchi, Gilberto Pastorello, Payton Linton, Devarshi Ghoshal, William Melodia, Kesheng Wu

Publikováno v: IEEE BigData

Today, scientific experiments and simulations produce massive amounts of heterogeneous data that need to be stored and analyzed. Given that these large datasets are stored in many files, formats and locations, how can scientists find relevant data, d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e13d0329ed6de78b90d3c5f72ade31ec
https://escholarship.org/uc/item/8193v6sm