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BiDaML in Practice: Collaborative Modeling of Big Data Analytics Application Requirements

Version 2 2024-06-04, 13:05
Version 1 2021-04-15, 18:16
conference contribution
posted on 2024-06-04, 13:05 authored by Hourieh KhalajzadehHourieh Khalajzadeh, AJ Simmons, T Verma, Mohamed AbdelrazekMohamed Abdelrazek, J Grundy, J Hosking, Q He, P Ratnakanthan, A Zia, M Law
Using data analytics to improve industrial planning and operations has become increasingly popular and data scientists are more and more in demand. However, complex data analytics-based software development is challenging. It involves many new roles lacking in traditional software engineering teams – e.g. data scientists and data engineers; use of sophisticated machine learning (ML) approaches replacing many programming tasks; uncertainty inherent in the models; as well as interfacing with models to fulfill software functionalities. These challenges make communication and collaboration within the team and with external stakeholders challenging. In this paper, we describe our experiences in applying our BiDaML (Big Data Analytics Modeling Languages) approach to several large-scale industrial projects. We used our BiDaML modeling toolset that brings all stakeholders around one tool to specify, model and document their big data applications. We report our experience in using and evaluating this tool on three real-world, large-scale applications with teams from: realas.com – a property price prediction website for home buyers; VicRoads – a project seeking to build a digital twin (simulated model) of Victoria’s transport network updated in real-time by a stream of sensor data from inductive loop detectors at traffic intersections; and the Alfred Hospital – Intracranial hemorrhage (ICH) prediction through Computed Tomography (CT) Scans. These show that our approach successfully supports complex data analytics software development in industrial settings.

History

Volume

1375

Pagination

106-129

Location

Prague, Czech Republic

Start date

2021-05-05

End date

2021-05-06

ISSN

1865-0929

eISSN

1865-0937

ISBN-13

9783030700058

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

Springer Nature Switzerland AG 2021

Editor/Contributor(s)

Ali R, Kaindl H, Maciaszek LA

Title of proceedings

ENASE 2020 : Evaluation of novel approaches to software engineering : 15th international conference, ENASE 2020, Prague, Czech Republic, May 5-6, 2020 : revised selected papers

Event

Evaluation of Novel Approaches to Software Engineering. Conference (2021 : 15th : Prague, Czech Republic)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Communications in Computer and Information Science

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