loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Hourieh Khalajzadeh 1 ; Andrew J. Simmons 2 ; Mohamed Abdelrazek 3 ; John Grundy 1 ; John Hosking 4 and Qiang He 5

Affiliations: 1 Faculty of Information Technology, Monash University, Australia ; 2 Applied Artificial Intelligence Institute (A2I2), Deakin University, Australia ; 3 School of Information Technology, Deakin University, Australia ; 4 Faculty of Science, University of Auckland, New Zealand ; 5 School of Software and Electrical Engineering, Swinburne University of Technology, Australia

Keyword(s): Big Data Analytics, Big Data Modeling, Big Data Toolkits, Domain Specific Visual Languages, End-user Tools.

Abstract: We present BiDaML (Big Data Analytics Modeling Languages), an integrated suite of visual languages and supporting tool to help end-users with the engineering of big data analytics solutions. BiDaML, our visual notations suite, comprises six diagrammatic notations: brainstorming diagram, process diagram, technique diagrams, data diagrams, output diagrams and deployment diagram. BiDaML tool provides a platform for efficiently producing BiDaML visual models and facilitating their design, creation, code generation and integration with other tools. To demonstrate the utility of BiDaML, we illustrate our approach with a real-world example of traffic data analysis. We evaluate BiDaML using two types of evaluations, the physics of notations and a cognitive walkthrough with several target end-users e.g. data scientists and software engineers.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.217.60.167

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Khalajzadeh, H. ; Simmons, A. ; Abdelrazek, M. ; Grundy, J. ; Hosking, J. and He, Q. (2020). Visual Languages for Supporting Big Data Analytics Development. In Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE; ISBN 978-989-758-421-3; ISSN 2184-4895, SciTePress, pages 15-26. DOI: 10.5220/0009192900150026

@conference{enase20,
author={Hourieh Khalajzadeh and Andrew J. Simmons and Mohamed Abdelrazek and John Grundy and John Hosking and Qiang He},
title={Visual Languages for Supporting Big Data Analytics Development},
booktitle={Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE},
year={2020},
pages={15-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009192900150026},
isbn={978-989-758-421-3},
issn={2184-4895},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - ENASE
TI - Visual Languages for Supporting Big Data Analytics Development
SN - 978-989-758-421-3
IS - 2184-4895
AU - Khalajzadeh, H.
AU - Simmons, A.
AU - Abdelrazek, M.
AU - Grundy, J.
AU - Hosking, J.
AU - He, Q.
PY - 2020
SP - 15
EP - 26
DO - 10.5220/0009192900150026
PB - SciTePress