A practical, collaborative approach for modeling big data analytics application requirements

Hourieh Khalajzadeh; Andrew Simmons; Mohamed Abdelrazek; John Grundy; John Hosking; Qiang He; Prasanna Ratnakanthan; Adil Zia; Meng Law

doi:10.1145/3377812.3390811

A practical, collaborative approach for modeling big data analytics application requirements

Hourieh Khalajzadeh, Andrew Simmons, Mohamed Abdelrazek, John Grundy, John Hosking, Qiang He, Prasanna Ratnakanthan, Adil Zia, Meng Law

Department of Software Systems & Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

Abstract

Data analytics application development introduces many challenges including: New roles not in traditional software engineering practices-e.g. data scientists and data engineers; use of sophisticated machine learning (ML) model-based approaches; uncertainty inherent in the models; interfacing with models to fulfill software functionalities; deploying models at scale and rapid evolution of business goals and data sources. We describe our Big Data Analytics Modeling Languages (BiDaML) toolset to bring all stakeholders around one tool to specify, model and document big data applications. We report on our experience applying BiDaML to three real-world large-scale applications. Our approach successfully supports complex data analytics application development in industrial settings.

Original language	English
Title of host publication	Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering
Subtitle of host publication	Companion Proceedings, ICSE-Companion 2020
Editors	Gregg Rothermel, Doo-Hwan Bae
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	256-257
Number of pages	2
ISBN (Electronic)	9781450371223
DOIs	https://doi.org/10.1145/3377812.3390811
Publication status	Published - Jun 2020
Event	International Conference on Software Engineering 2020 - Online, Seoul, Korea, Republic of (South) Duration: 27 Jun 2020 → 19 Jul 2020 Conference number: 42nd https://dl.acm.org/doi/proceedings/10.1145/3377811 (Proceedings) https://conf.researchr.org/home/icse-2020 (Website)

Conference

Conference	International Conference on Software Engineering 2020
Abbreviated title	ICSE 2020
Country/Territory	Korea, Republic of (South)
City	Seoul
Period	27/06/20 → 19/07/20
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3377811 (Proceedings) https://conf.researchr.org/home/icse-2020 (Website)

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10.1145/3377812.3390811

Cite this

Khalajzadeh, H., Simmons, A., Abdelrazek, M., Grundy, J., Hosking, J., He, Q., Ratnakanthan, P., Zia, A., & Law, M. (2020). A practical, collaborative approach for modeling big data analytics application requirements. In G. Rothermel, & D-H. Bae (Eds.), Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2020 (pp. 256-257). Association for Computing Machinery (ACM). https://doi.org/10.1145/3377812.3390811

Khalajzadeh, Hourieh ; Simmons, Andrew ; Abdelrazek, Mohamed ; Grundy, John ; Hosking, John ; He, Qiang ; Ratnakanthan, Prasanna ; Zia, Adil ; Law, Meng. / A practical, collaborative approach for modeling big data analytics application requirements. Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2020. editor / Gregg Rothermel ; Doo-Hwan Bae. New York NY USA : Association for Computing Machinery (ACM), 2020. pp. 256-257

@inproceedings{955973b483bc41f9b655653aedde5552,

title = "A practical, collaborative approach for modeling big data analytics application requirements",

abstract = "Data analytics application development introduces many challenges including: New roles not in traditional software engineering practices-e.g. data scientists and data engineers; use of sophisticated machine learning (ML) model-based approaches; uncertainty inherent in the models; interfacing with models to fulfill software functionalities; deploying models at scale and rapid evolution of business goals and data sources. We describe our Big Data Analytics Modeling Languages (BiDaML) toolset to bring all stakeholders around one tool to specify, model and document big data applications. We report on our experience applying BiDaML to three real-world large-scale applications. Our approach successfully supports complex data analytics application development in industrial settings. ",

author = "Hourieh Khalajzadeh and Andrew Simmons and Mohamed Abdelrazek and John Grundy and John Hosking and Qiang He and Prasanna Ratnakanthan and Adil Zia and Meng Law",

note = "Funding Information: Support from ARC Discovery Project DP170101932 and ARC Laureate Program FL190100035 is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2020 ACM.; International Conference on Software Engineering 2020, ICSE 2020 ; Conference date: 27-06-2020 Through 19-07-2020",

year = "2020",

month = jun,

doi = "10.1145/3377812.3390811",

language = "English",

pages = "256--257",

editor = "Rothermel, {Gregg } and Bae, {Doo-Hwan }",

booktitle = "Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://dl.acm.org/doi/proceedings/10.1145/3377811, https://conf.researchr.org/home/icse-2020",

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Khalajzadeh, H, Simmons, A, Abdelrazek, M, Grundy, J, Hosking, J, He, Q, Ratnakanthan, P, Zia, A & Law, M 2020, A practical, collaborative approach for modeling big data analytics application requirements. in G Rothermel & D-H Bae (eds), Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2020. Association for Computing Machinery (ACM), New York NY USA, pp. 256-257, International Conference on Software Engineering 2020, Seoul, Korea, Republic of (South), 27/06/20. https://doi.org/10.1145/3377812.3390811

A practical, collaborative approach for modeling big data analytics application requirements. / Khalajzadeh, Hourieh; Simmons, Andrew; Abdelrazek, Mohamed; Grundy, John; Hosking, John; He, Qiang; Ratnakanthan, Prasanna; Zia, Adil; Law, Meng.

Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2020. ed. / Gregg Rothermel; Doo-Hwan Bae. New York NY USA : Association for Computing Machinery (ACM), 2020. p. 256-257.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Khalajzadeh, Hourieh

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AU - Abdelrazek, Mohamed

AU - Grundy, John

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AU - He, Qiang

AU - Ratnakanthan, Prasanna

AU - Zia, Adil

AU - Law, Meng

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AB - Data analytics application development introduces many challenges including: New roles not in traditional software engineering practices-e.g. data scientists and data engineers; use of sophisticated machine learning (ML) model-based approaches; uncertainty inherent in the models; interfacing with models to fulfill software functionalities; deploying models at scale and rapid evolution of business goals and data sources. We describe our Big Data Analytics Modeling Languages (BiDaML) toolset to bring all stakeholders around one tool to specify, model and document big data applications. We report on our experience applying BiDaML to three real-world large-scale applications. Our approach successfully supports complex data analytics application development in industrial settings.

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M3 - Conference Paper

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BT - Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering

A2 - Rothermel, Gregg

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PB - Association for Computing Machinery (ACM)

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ER -

Khalajzadeh H, Simmons A, Abdelrazek M, Grundy J, Hosking J, He Q et al. A practical, collaborative approach for modeling big data analytics application requirements. In Rothermel G, Bae D-H, editors, Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion Proceedings, ICSE-Companion 2020. New York NY USA: Association for Computing Machinery (ACM). 2020. p. 256-257 https://doi.org/10.1145/3377812.3390811

A practical, collaborative approach for modeling big data analytics application requirements

Abstract

Conference

Access to Document

Other files and links

HCMDSE: Human-centric Model-driven Software Engineering

Domain-specific visual languages for big data analytics applications

Cite this

A practical, collaborative approach for modeling big data analytics application requirements

Abstract

Conference

Access to Document

Other files and links

Projects

HCMDSE: Human-centric Model-driven Software Engineering

Domain-specific visual languages for big data analytics applications

Cite this