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Khalajzadeh, H., Simmons, A.J., Abdelrazek, M., Grundy, J., Hosking, J., He, Q.: End-user-oriented tool support for modeling data analytics requirements. Khalajzadeh, H., Simmons, A.J., Abdelrazek, M., Grundy, J., Hosking, J., He, Q.: An end-to-end model-based approach to support big data analytics development. Khalajzadeh, H., Abdelrazek, M., Grundy, J., Hosking, J., He, Q.: Survey and analysis of current end-user data analytics tool support. In: 2019 IEEE International Congress on Big Data (BigDataCongress), pp. Khalajzadeh, H., Abdelrazek, M., Grundy, J., Hosking, J., He, Q.: BiDaML: a suite of visual languages for supporting end-user data analytics. In: 2018 IEEE International Congress on Big Data (BigData Congress), pp. Khalajzadeh, H., Abdelrazek, M., Grundy, J., Hosking, J., He, Q.: A survey of current end-user data analytics tool support. Kamalrudin, M., Hosking, J., Grundy, J.: MaramaAIC: tool support for consistency management and validation of requirements. 669–675 (2015)ĭwyer, T., Marriott, K., Wybrow, M.: Dunnart: a constraint-based network diagram authoring tool. In: 21st International Congress on Modelling and Simulation’, Broadbeach, Queensland, Australia, pp. 745–747 (2006)Ĭleary, P., Thomas, D., Bolger, M., Hetherton, L., Rucinski, C., Watkins, D.: Using workspace to automate workflow processes for modelling and simulation in engineering. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, pp. IEEE (2014)Ĭallahan, S.P., Freire, J., Santos, E., Scheidegger, C.E., Silva, C.T., Vo, H.T.: VisTrails: visualization meets data management. In: 2014 47th Hawaii International Conference on System Sciences, pp. 371(1984), 20120222 (2013)īreuker, D.: Towards model-driven engineering for big data analytics-an exploratory analysis of domain-specific languages for machine learning. īaker, M.: 1,500 scientists lift the lid on reproducibility (2016)īishop, C.M.: Model-based machine learning. KeywordsīiDaML big data analytics modeling languages.
Data model capture projects tasks and subtasks software#
These show that our approach successfully supports complex data analytics software development in industrial settings.
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We report our experience in using and evaluating this tool on three real-world, large-scale applications with teams from: – 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. We used our BiDaML modeling toolset that brings all stakeholders around one tool to specify, model and document their big data applications. In this paper, we describe our experiences in applying our BiDaML (Big Data Analytics Modeling Languages) approach to several large-scale industrial projects. These challenges make communication and collaboration within the team and with external stakeholders challenging. 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. It involves many new roles lacking in traditional software engineering teams – e.g. However, complex data analytics-based software development is challenging. Using data analytics to improve industrial planning and operations has become increasingly popular and data scientists are more and more in demand.