Addressing big data issues in Scientific Data Infrastructure

By Y. Demchenko, P. Grosso, Cees de Laat, Peter Membrey. 2013

504 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems and Management, Management Science and Operations Research

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In Computer Networks and Communications

In Information Systems and Management

In Management Science and Operations Research

Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, background): “Several works have described the general features of big data from different aspects [14]–[17] in terms of volume, velocity, and variety.”
Assessing the impact of big data on firm innovation performance: Big data is not always better data (2020, background): “Thus, although some studies have considered the size of data as the main characteristics of big data (Demchenko, Grosso, De Laat, & Membrey, 2013), one of our main contributions is to show that other big data main characteristics (i.e., variety, and velocity) play more vital roles in enhancing firm…”
The Challenges of Data Quality and Data Quality Assessment in the Big Data Era (2015, background): “…and mobile internet (Li & Liu, 2013); 2) data from the Internet of Things; 3) data collected by various industries; 4) scientific experimental and observational data (Demchenko, Grosso & Laat, 2013), such as high-energy physics experimental data, biological data, and space observation data.”
Big data analytics capabilities: a systematic literature review and research agenda (2017, background): “Veracity refers to the degree to which big data is trusted, authentic, and protected from unauthorized access and modiﬁcation (Demchenko et al. 2013).”
Defining architecture components of the Big Data Ecosystem (2014, unknown): “Customers must trust the BDI to process their data on BDI facilities and be ensured that their stored research data are protected from non-authorised access.”
Machine Learning With Big Data: Challenges and Approaches (2017, background): “For example, value is often added as a 5th V [22], [26].”
Big Data: Survey, Technologies, Opportunities, and Challenges (2014, background): “The generic life cycle of scientific data is composed of sequential stages, including experiment planning (research project), data collection and processing, discussion, feedback, and archiving [58–60].”