An end-to-end model-based approach to support big data analytics development

Khalajzadeh, Hourieh; Simmons, Anj; Abdelrazek, Mohamed; Grundy, John; Hosking, John; He, Qiang

doi:10.1016/j.cola.2020.100964

Cited by 15 publications

(11 citation statements)

References 9 publications

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“…Moreover, most end-users in multidisciplinary teams have limited technical knowledge of data science and programming and thus usually struggle to use these tools. Therefore, a high-level presentation of the steps to capture, represent and communicate the business requirements analysis and design, data pre-processing, high-level data analysis process, solution deployment, and data visualisation is essential [6].…”

Section: Data Analytics Toolsmentioning

confidence: 99%

“…Big Data Analytics Modeling Languages (BiDaML) is a a set of domain-specific visual languages using five diagram types at different levels of abstraction to support key aspects of big data analytics [4,6]. These five diagram types cover the whole of the data analytics software development life cycle from higher-level requirement analysis and problem definition through the low-level deployment of the final product.…”

Section: Bidamlmentioning

confidence: 99%

“…BiDaML supports aspects of Model-Driven software development, such as validation rules for how elements may be connected, automatic report generation, and (limited) functionality for code generation from a diagram. In contrast to other modelling languages such as UML and BPMN, BiDaML was designed from the ground up using the Physics of Notations [10] framework to be Cognitively Effective for novice users, and has been validated though user studies [6] to be suitable for a diverse audience.…”

Section: Bidamlmentioning

confidence: 99%

“…In contrast to the original BiDaML tool [6], we wanted BiDaMLweb to provide an integrated environment for design of big data systems. BiDaML-web includes both a paper and dataset recommender tool integrated into the environment itself.…”

Section: Recommender Toolmentioning

confidence: 99%

“…As big data projects have their own set of concerns beyond traditional software engineering projects and require engagement from diverse stakeholders [7,15], there is a need for domain specific modelling languages and associated tooling to address the needs of big data projects. One such attempt is the Big Data Analytics Modeling Languages (BiDaML) [6], which provides a metamodel and associated set of visual notations for end-to-end requirements analysis of big data problems, and was designed to be comprehensible to a diverse set of stakeholders. However, while users responded positively to the language and notations themselves, they responded poorly to the tool used to support the modelling language.…”

Section: Introductionmentioning

confidence: 99%

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User-centred tooling for modelling of big data applications

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Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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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. CCS CONCEPTS • Software and its engineering → Visual languages; Requirements analysis; • Information systems → Data mining; • Humancentered computing → Human computer interaction (HCI).

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Section: Data Analytics Toolsmentioning

confidence: 99%

Section: Bidamlmentioning

confidence: 99%