Related papers: Ten Lessons for Data Sharing With a Data Commons
Cloud Computing refers to the use of computer resources as a service on-demand via internet. It is mainly based on data and applications outsourcing, traditionally stored on users' computers, to remote servers (datacenters) owned,…
This letter provides a review of fundamental distributed systems and economic Cloud computing principles. These principles are frequently deployed in their respective fields, but their inter-dependencies are often neglected. Given that…
This paper proposes a data privacy protection framework based on federated learning, which aims to realize effective cross-domain data collaboration under the premise of ensuring data privacy through distributed learning. Federated learning…
Data grid is a distributed computing architecture that integrates a large number of data and computing resources into a single virtual data management system. It enables the sharing and coordinated use of data from various resources and…
Automated cyber threat detection in computer networks is a major challenge in cybersecurity. The cyber domain has inherent challenges that make traditional machine learning techniques problematic, specifically the need to learn continually…
While collaboration among data scientists is a key to organizational productivity, data analysts face significant barriers to achieving this end, including data sharing, accessing and configuring the required computational environment, and…
Cloud Computing is rising fast, with its data centres growing at an unprecedented rate. However, this has come with concerns over privacy, efficiency at the expense of resilience, and environmental sustainability, because of the dependence…
Sharing scientific data, with the objective of making it fully discoverable, accessible, assessable, intelligible, usable, and interoperable, requires work at the disciplinary level to define in particular how the data should be formatted…
The excessive amounts of data generated by devices and Internet-based sources at a regular basis constitute, big data. This data can be processed and analyzed to develop useful applications for specific domains. Several mathematical and…
Concurrent computations resemble conversations. In a conversation, participants direct utterances at others and, as the conversation evolves, exploit the known common context to advance the conversation. Similarly, collaborating software…
In recent years, various kinds of distributed resource sharing setups have been proposed by taking social relationships into consideration. These dissimilar resource sharing setups are tagged as Social Cloud. These setups have appeared in…
Implementation plans for the National Institutes of Health policy for data management and sharing, which takes effect in 2023, provide an opportunity to reflect on the stakeholders, infrastructures, practice, economics, and sustainability…
Data sharing is the fuel of the galloping artificial intelligence economy, providing diverse datasets for training robust models. Trust between data providers and data consumers is widely considered one of the most important factors for…
The knowledge commons research framework is applied to a case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place…
Machine Learning systems rely on data for training, input and ongoing feedback and validation. Data in the field can come from varied sources, often anonymous or unknown to the ultimate users of the data. Whenever data is sourced and used,…
Cloud computing allows users to view computing in a new direction, as it uses the existing technologies to provide better IT services at low-cost. To offer high QOS to customers according SLA, cloud services broker or cloud service provider…
Nowadays, the utilization of the ever expanding amount of data has made a huge impact on web technologies while also causing various types of security concerns. On one hand, potential gains are highly anticipated if different organizations…
Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving…
We analyze a distributed information network in which each node has access to the information contained in a limited set of nodes (its neighborhood) at a given time. A collective computation is carried out in which each node calculates a…
The twenty-first century is a data-driven era where human activities and behavior, physical phenomena, scientific discoveries, technology advancements, and almost everything that happens in the world resulting in massive generation,…