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 |
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| Přispěvatelé: | Computer Systems, Network Institute, High Performance Distributed Computing, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny |
| Rok vydání: | 2019 |
| Předmět: |
SDG 16 - Peace
Computer science business.industry SDG 16 - Peace Justice and Strong Institutions Big data 020207 software engineering Cloud computing 02 engineering and technology computer.software_genre Justice and Strong Institutions Software framework Human–computer interaction 020204 information systems 0202 electrical engineering electronic engineering information engineering Use case Cloudlet business computer |
| Zdroj: | 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 |
| DOI: | 10.1109/bigdata47090.2019.9006113 |
| Popis: | 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 smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms. |
| Databáze: | OpenAIRE |
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