Predicting outcomes for big data projects: Big Data Project Dynamics (BDPD): Research in progress

Becker, David K.

doi:10.1109/bigdata.2017.8258186

Cited by 11 publications

(14 citation statements)

References 3 publications

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“…Hospitals and clinics are constrained to storing and analyzing medical data using traditional and manual methods. Many medical institutions have made significant efforts to overcome this limitation by combining considerable data resources with new technologies [ 1 ]; however, numerous medical facilities failed to implement new systems early. There is still a lack of knowledge about diseases and how to treat them, despite the enormous number of data available.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

El‐Hasnony

Elzeki

Alshehri

et al. 2022

Sensors

102

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The rapid growth and adaptation of medical information to identify significant health trends and help with timely preventive care have been recent hallmarks of the modern healthcare data system. Heart disease is the deadliest condition in the developed world. Cardiovascular disease and its complications, including dementia, can be averted with early detection. Further research in this area is needed to prevent strokes and heart attacks. An optimal machine learning model can help achieve this goal with a wealth of healthcare data on heart disease. Heart disease can be predicted and diagnosed using machine-learning-based systems. Active learning (AL) methods improve classification quality by incorporating user–expert feedback with sparsely labelled data. In this paper, five (MMC, Random, Adaptive, QUIRE, and AUDI) selection strategies for multi-label active learning were applied and used for reducing labelling costs by iteratively selecting the most relevant data to query their labels. The selection methods with a label ranking classifier have hyperparameters optimized by a grid search to implement predictive modelling in each scenario for the heart disease dataset. Experimental evaluation includes accuracy and F-score with/without hyperparameter optimization. Results show that the generalization of the learning model beyond the existing data for the optimized label ranking model uses the selection method versus others due to accuracy. However, the selection method was highlighted in regards to the F-score using optimized settings.

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Section: Introductionmentioning

confidence: 99%

Multi-Label Active Learning-Based Machine Learning Model for Heart Disease Prediction

El‐Hasnony

Elzeki

Alshehri

et al. 2022

Sensors

102

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“…In addition, [48] points out ineffective governance models for data analytics and [43] emphasize inadequate management and a lack of sponsorship from top management side. In this context, [49] affirm that working in confusing, chaotic environments can be frustrating and may lower team members' motivation and their ability to focus on the project objectives.…”

Section: Coordination Collaboration and Communicationmentioning

confidence: 99%

“…An increase in data volume and velocity intensifies the computation requirements and hence the dependence of the project on IT resources [8]. In addition, the scale of the data magnifies the technology complexity and the necessary architecture and infrastructure [48] and as a result the corresponding costs [43].…”

Section: Driving With Datamentioning

confidence: 99%

“…Apart from lack of coordination, for [44,45,46] there are clear issues of collaboration and [47,48] highlight a lack of transparent communication between the three main stakeholders: business (client), analytics team and the IT department. For instance, [44] mentions the difficulty for analytics teams to deploy to production, coordinate with the IT department and explain data science to business partners.…”

Section: Coordination Collaboration and Communicationmentioning

confidence: 99%

“…In other terms, [50] brings out problems to engage the proper team for the project and [45,46,43,48,51] highlight the lack of people with analytics skills. These shortages of specialized analytical labor have caused every major university to launch new big data, analytics or data science programs [42].…”

Section: Building Data Analytics Teamsmentioning

confidence: 99%

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Data Science Methodologies: Current Challenges and Future Approaches

2021

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Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

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