Data science: a game changer for science and innovation

Grossi, Valerio; Giannotti, Fosca; Pedreschi, Dino; Manghi, Paolo; Pagano, Pasquale; Assante, Massimiliano

doi:10.1007/s41060-020-00240-2

Cited by 26 publications

(20 citation statements)

References 41 publications

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“…The NAVIGATOR VRE will be delivered as an extension and customisation of the D4Science.org infrastructure, a well-established production-ready technology today supporting the operation of various European H2020 and Horizon Europe Research Infrastructures ( e.g., SoBigData.eu [ 38 ], AriadnePlus [ https://ariadne-infrastructure.eu/ ], BlueCloud [ https://blue-cloud.org/ ], AGINFRA [ https://plus.aginfra.eu/ ]). This infrastructure is in turn powered by the gCube software toolkit 1 .…”

Section: Platform Implementationmentioning

confidence: 99%

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients

Borgheresi

Barucci²,

Colantonio³

et al. 2022

Eur Radiol Exp

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NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalised by advancing translational research based on quantitative imaging and integrative omics analyses. The project’s goal is to develop an open imaging biobank for the collection and preservation of a large amount of standardised imaging multimodal datasets, including computed tomography, magnetic resonance imaging, and positron emission tomography data, together with the corresponding patient-related and omics-related relevant information extracted from regional healthcare services using an adapted privacy-preserving model. The project is based on an open-source imaging biobank and an open-science oriented virtual research environment (VRE). Available integrative omics and multi-imaging data of three use cases (prostate cancer, rectal cancer, and gastric cancer) will be collected. All data confined in NAVIGATOR (i.e., standard and novel imaging biomarkers, non-imaging data, health agency data) will be used to create a digital patient model, to support the reliable prediction of the disease phenotype and risk stratification. The VRE that relies on a well-established infrastructure, called D4Science.org, will further provide a multiset infrastructure for processing the integrative omics data, extracting specific radiomic signatures, and for identification and testing of novel imaging biomarkers through big data analytics and artificial intelligence.

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Section: Platform Implementationmentioning

confidence: 99%

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients

Borgheresi

Barucci²,

Colantonio³

et al. 2022

Eur Radiol Exp

Self Cite

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“…SLR Scope/Focus [4] "A systematic literature review on maturity models that assess the level of maturity for smart city projects" [11] "A systematic literature survey on software architectures for big data systems", with a few connections with SC [12] "IoT challenges in smart cities and provide the gap between the existing state-of-the-art IoT application on S" [13] "A comprehensive analysis of the literature on interoperability of SC data platforms" [10] "Analyze the link between the concepts of smart cities, machine learning techniques and their applicability" [14] "Systematically investigated the evolution of OGD research" [15] "the relationship between big and open data and how they relate to the broad concept of open government" [16] "Covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities" and concludes that "open data movement has increased the number of research works in the field of machine learning, especially in the prediction of air quality". [17] "Comprehensive survey that explores the application of graph neural networks for traffic forecasting problems", presenting also "a comprehensive list of open data and source resources for each problem" [18] "The challenges faced by smart cities and the key role data mining, machine learning and statistical methods can play to enable intelligent solutions for different applications" [19] "Systematically reviews the top 200 Google Scholar publications in the area of smart city with the aid of data-driven methods from the fields natural language processing and time series forecasting" [7] "Generates insights into how AI can contribute to the development of smarter cities".…”

Section: Papermentioning

confidence: 99%

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

et al. 2021

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Machine learning (ML) has already gained the attention of the researchers involved in smart city (SC) initiatives, along with other advanced technologies such as IoT, big data, cloud computing, or analytics. In this context, researchers also realized that data can help in making the SC happen but also, the open data movement has encouraged more research works using machine learning. Based on this line of reasoning, the aim of this paper is to conduct a systematic literature review to investigate open data-based machine learning applications in the six different areas of smart cities. The results of this research reveal that: (a) machine learning applications using open data came out in all the SC areas and specific ML techniques are discovered for each area, with deep learning and supervised learning being the first choices. (b) Open data platforms represent the most frequently used source of data. (c) The challenges associated with open data utilization vary from quality of data, to frequency of data collection, to consistency of data, and data format. Overall, the data synopsis as well as the in-depth analysis may be a valuable support and inspiration for the future smart city projects.

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“…On the one hand, in the era of big data, different data forms such as audio, video, pictures, logs, networks, and locations present a trend of linear growth in data. On the other hand, real time processing is the mainstream form of big data processing, and the requirements for processing speed are becoming more and more stringent [2].…”

Section: Introductionmentioning

confidence: 99%

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

Shi

Zhang

et al. 2022

Int. J. Emerg. Technol. Learn.

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With the development of the Internet, various forms of learning resources continue to flood the public, especially the promotion of video learning plat-forms. More and more people use their spare time to learn what they need. However, there is a general lack of intelligence in online learning platforms at present, which greatly reduced the utilization of online learning platforms and their educational advantages. The innovation of this paper is proposing and explaining that with the integration of big data, online education and ar-tificial intelligence, the contradiction of online education has turned into one between the lack of intelligence in online education and the demand of users. To solve this contradiction, this paper researches from the perspective of an algorithm. Course recommendation is the core algorithm of online ed-ucation. However, the current recommendation algorithm based on collabo-rative filtering has the disadvantages of cold start and useless recommenda-tion content. In this paper, Apriori and ACO algorithms in artificial intelli-gence are studied, and the proposal of an algorithmic framework named Posi-tion-Apriori-ACO brings forth new ideas in solving online education prob-lems. The Position-Apriori-ACO algorithm can effectively carry out course recommendation and learning path planning, and also provides a research di-rection for the intelligent development of online education.

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Data science: a game changer for science and innovation

Cited by 26 publications

References 41 publications

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients

Open Data Based Machine Learning Applications in Smart Cities: A Systematic Literature Review

Learning Path Planning Algorithm Based on Career Goals and Artificial Intelligence

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