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

Dept of Software Systems & Cybersecurity

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

Abstract

Data analytics application development introduces manychallenges including: new roles not in traditional softwareengineering practices e.g. data scientists and data engineers; useof sophisticated machine learning (ML) model-based approaches;uncertainty inherent in the models; interfacing with models tofulfill software functionalities; deploying models at scale andrapid evolution of business goals and data sources. We describeour Big Data Analytics Modeling Languages (BiDaML) toolset tobring all stakeholders around one tool to specify, model anddocument big data applications. We report on our experienceapplying BiDaML to three real-world large-scale applications.Our approach successfully supports complex data analyticsapplication 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 - Virtual, Online, 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	Korea, Republic of (South)
City	Virtual, Online
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)

Access to Document

10.1145/3377812.3390811

Link to publication in Scopus

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). [3390811] 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

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

abstract = "Data analytics application development introduces manychallenges including: new roles not in traditional softwareengineering practices e.g. data scientists and data engineers; useof sophisticated machine learning (ML) model-based approaches;uncertainty inherent in the models; interfacing with models tofulfill software functionalities; deploying models at scale andrapid evolution of business goals and data sources. We describeour Big Data Analytics Modeling Languages (BiDaML) toolset tobring all stakeholders around one tool to specify, model anddocument big data applications. We report on our experienceapplying BiDaML to three real-world large-scale applications.Our approach successfully supports complex data analyticsapplication 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",

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",

note = "International Conference on Software Engineering 2020, ICSE 2020 ; Conference date: 27-06-2020 Through 19-07-2020",

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., 3390811, Association for Computing Machinery (ACM), New York NY USA, pp. 256-257, International Conference on Software Engineering 2020, Virtual, Online, 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 3390811.

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

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T1 - A practical, collaborative approach for modeling big data analytics application requirements

AU - Khalajzadeh, Hourieh

AU - Simmons, Andrew

AU - Abdelrazek, Mohamed

AU - Grundy, John

AU - Hosking, John

AU - He, Qiang

AU - Ratnakanthan, Prasanna

AU - Zia, Adil

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AB - Data analytics application development introduces manychallenges including: new roles not in traditional softwareengineering practices e.g. data scientists and data engineers; useof sophisticated machine learning (ML) model-based approaches;uncertainty inherent in the models; interfacing with models tofulfill software functionalities; deploying models at scale andrapid evolution of business goals and data sources. We describeour Big Data Analytics Modeling Languages (BiDaML) toolset tobring all stakeholders around one tool to specify, model anddocument big data applications. We report on our experienceapplying BiDaML to three real-world large-scale applications.Our approach successfully supports complex data analyticsapplication development in industrial settings.

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SP - 256

EP - 257

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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T2 - International Conference on Software Engineering 2020

Y2 - 27 June 2020 through 19 July 2020

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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. 3390811 https://doi.org/10.1145/3377812.3390811