User-centred tooling for modelling of Big Data applications

Hourieh Khalajzadeh, Tarun Verma, Andrew J. Simmons, John Grundy, Mohamed Abdelrazek, John Hosking

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

1 Citation (Scopus)

Abstract

We outline the key requirements for a Big Data modelling recommender tool. Our web-based tool is suitable for capturing system requirements in big data analytics applications involving diverse stakeholders. It promotes awareness of the datasets and algorithm implementations that are available to leverage in the design of the solution. We implement these ideas in BiDaML-web, a proof of concept recommender system for Big Data applications, and evaluate the tool using an empirical study with a group of 16 target end-users. Participants found the integrated recommender and technique suggestion tools helpful and highly rated the overall BiDaML web-based modelling experience. BiDaML-web is available at https://bidaml.web.app/ and the source code can be accessed at https://github.com/tarunverma23/bidaml.

Original languageEnglish
Title of host publicationProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings, MODELS-C 2020
EditorsBenoit Combemale, Manuel Wimmer
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages31-35
Number of pages5
ISBN (Electronic)9781450381352
DOIs
Publication statusPublished - 2020
EventACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2020 - Virtual, Montreal, Canada
Duration: 16 Oct 202023 Oct 2020
Conference number: 23rd
https://dl-acm-org.ezproxy.lib.monash.edu.au/doi/proceedings/10.1145/3417990 (Proceedings)
https://conf.researchr.org/home/models-2020 (Website)

Conference

ConferenceACM/IEEE International Conference on Model Driven Engineering Languages and Systems 2020
Abbreviated titleMODELS-C 2020
CountryCanada
CityMontreal
Period16/10/2023/10/20
Internet address

Keywords

  • BiDaML
  • Big data applications
  • Recommender

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