A Practical, Collaborative Approach for Modeling Big Data Analytics Application Requirements
Version 2 2024-06-04, 13:05Version 2 2024-06-04, 13:05
Version 1 2020-11-04, 07:02Version 1 2020-11-04, 07:02
conference contribution
posted on 2024-06-04, 13:05 authored by Hourieh KhalajzadehHourieh Khalajzadeh, A Simmons, Mohamed AbdelrazekMohamed Abdelrazek, J Grundy, J Hosking, Q He, P Ratnakanthan, A Zia, M Law© 2020 Copyright held by the owner/author(s). 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.
History
Pagination
256-257Location
ELECTR NETWORKPublisher DOI
Start date
2020-06-27End date
2020-07-19ISSN
0270-5257ISBN-13
9781450371223Language
EnglishPublication classification
E3 Extract of paperTitle of proceedings
Proceedings - 2020 ACM/IEEE 42nd International Conference on Software Engineering: Companion, ICSE-Companion 2020Event
42nd ACM/IEEE International Conference on Software Engineering - Companion Proceedings (ICSE-Companion)Publisher
IEEESeries
International Conference on Software EngineeringUsage metrics
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