A Programming Framework for Heterogeneous Stream Analytics

Roshan Bharath Das; Marc X. Makkes; Alexandru Uta; Lin Wang; Henri Bal

doi:10.1109/BigData47090.2019.9006113

A Programming Framework for Heterogeneous Stream Analytics

Roshan Bharath Das, Marc X. Makkes, Alexandru Uta, Lin Wang, Henri Bal

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

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.

Original language	English
Title of host publication	2019 IEEE International Conference on Big Data (Big Data 2019)
Subtitle of host publication	[Proceedings]
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6030-6032
Number of pages	3
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006113
Publication status	Published - 24 Feb 2020
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: 9 Dec 2019 → 12 Dec 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	9/12/19 → 12/12/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BigData47090.2019.9006113

Persistent URL (handle)

Persistent link

Cite this

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] (pp. 6030-6032). Article 9006113 Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006113

Das, Roshan Bharath ; Makkes, Marc X. ; Uta, Alexandru et al. / A Programming Framework for Heterogeneous Stream Analytics. 2019 IEEE International Conference on Big Data (Big Data 2019): [Proceedings]. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 6030-6032

@inproceedings{b6d2816dc4844010b5bb203209b839ab,

title = "A Programming Framework for Heterogeneous Stream Analytics",

abstract = "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.",

author = "Das, {Roshan Bharath} and Makkes, {Marc X.} and Alexandru Uta and Lin Wang and Henri Bal",

year = "2020",

month = feb,

day = "24",

doi = "10.1109/BigData47090.2019.9006113",

language = "English",

pages = "6030--6032",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "2019 IEEE International Conference on Big Data (Big Data 2019)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

}

Das, RB, Makkes, MX, Uta, A, Wang, L & Bal, H 2020, A Programming Framework for Heterogeneous Stream Analytics. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF 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

A Programming Framework for Heterogeneous Stream Analytics. / Das, Roshan Bharath; Makkes, Marc X.; Uta, Alexandru et al.
2019 IEEE International Conference on Big Data (Big Data 2019): [Proceedings]. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2020. p. 6030-6032 9006113.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A Programming Framework for Heterogeneous Stream Analytics

AU - Das, Roshan Bharath

AU - Makkes, Marc X.

AU - Uta, Alexandru

AU - Wang, Lin

AU - Bal, Henri

PY - 2020/2/24

Y1 - 2020/2/24

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85081390402&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85081390402&partnerID=8YFLogxK

U2 - 10.1109/BigData47090.2019.9006113

DO - 10.1109/BigData47090.2019.9006113

M3 - Conference contribution

SP - 6030

EP - 6032

BT - 2019 IEEE International Conference on Big Data (Big Data 2019)

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Das RB, Makkes MX, Uta A, Wang L , Bal H. A Programming Framework for Heterogeneous Stream Analytics. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, 2019 IEEE International Conference on Big Data (Big Data 2019): [Proceedings]. Institute of Electrical and Electronics Engineers Inc. 2020. p. 6030-6032. 9006113 Epub 2019 Dec 9. doi: 10.1109/BigData47090.2019.9006113

A Programming Framework for Heterogeneous Stream Analytics

Abstract

Conference

UN SDGs

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Cite this