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Applications of Physics-Informed Scientific Machine Learning in Subsurface Science: A Survey
Published in Anuj Karpatne, Ramakrishnan Kannan, Vipin Kumar, Knowledge-Guided Machine Learning, 2023
Alexander Y. Sun, Hongkyu Yoon, Chung-Yan Shih, Zhi Zhong
Efforts from the public and private sectors have started to make data available. Government agencies encourage or require oil and gas operators to regularly report well information (e.g., drilling, completion, plugging, production, etc.) and make the data available to the public. Standing on the foundation, companies integrate the data and added proprietary assessments for commercial licenses. The U.S. Energy Information Administration (EIA) implements multiple approaches to facilitate data access (https://www.eia.gov/opendata). Over the last decade, the National Energy Technology Laboratory (NETL) has developed a data repository and laboratory, called Energy Data eXchange (EDX), to curate and preserve data for reuse and collaboration that supports the entire life cycle of data (https://edx.netl.doe.gov/). Open Energy Information (https://openei.org) represents an example of community-driven platform for sharing energy data. However, challenges related to data Findability, Accessibility, Interoperability, and Reuse (FAIR) remain to be solved. For example, government agencies or data publishers may have different definitions and data capturing processes. Comparing data on the same basis requires additional processing and deciphering (Lacley et al., 2021).
Recent Trends of IoT and Big Data in Research Problem-Solving
Published in Shivani Agarwal, Sandhya Makkar, Duc-Tan Tran, Privacy Vulnerabilities and Data Security Challenges in the IoT, 2020
Pham Thi Viet Huong, Tran Anh Vu
Web service is the core of the SIoT; therefore, web services’ improvement makes SIoT more feasible. Suitable policies are mentioned for the foundation and administration of social connections among objects in such a way that a social network can be navigable [66]. An IoT architecture is provided, which incorporates the necessary functionality to combine everything into a social network. Another web is proposed in [73] called Paraimpu, which is an architecture of a large-scale social website for smart objects and services. It is a web-enabled platform which permits most of the activities between real smart objects and virtual things like web services and social networks such as adding, sharing, and connecting. A social access controller platform is covered in [70], which can act as an authentication and sharing proxy for smart things that allow users to determine what action they need for their smart things. Moreover, this platform can be used for advertising shared smart things. Additionally, web solutions to four concerns—accessibility, findability, sharing, and composition—are also presented in [65].
User Experience and Information Architecture
Published in Marcelo M. Soares, Francisco Rebelo, Tareq Z. Ahram, Handbook of Usability and User Experience, 2022
Luiz Agner, Barbara Jane Necyk, Adriano Renzi
In this context, machine learning opened a new era for information architecture—considering findability in information-rich ambience. Its approaches are widely employed nowadays to analyze large bodies of data in cloud-based computing, extract patterns and support users' decision-making. As examples, we can cite the choice of a movie to watch on Netflix, a product to buy on Amazon or a song to listen to on Spotify. ML-based systems have significantly increased and lead us to question how these new techniques can meet users' information search needs while respecting human-centered design principles. For Wallach, Flohr and Kaltenhauser (2020), the UX discipline's traditional evaluation methods could underpin ML-based products' best development.
A digital curation model for post-occupancy evaluation data
Published in Architectural Engineering and Design Management, 2022
Panagiotis Patlakas, Marta Musso, Peter Larkham
These Principles were created by the FAIR Initiative, in order to establish the guidelines for data curation amongst researchers from academia and industry (Wilkinson et al., 2016). FAIR is the acronym for the four principles indicated by the consortium: Findability – both data and metadata must be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services.Accessibility – authentication and authorisation may be required; however, standardised communications protocols that are open, free, and universally accessible should be implemented.Interoperability – data and metadata need to be integrated with other data and with workflows for analysis, storage, and processing; they should use a broadly applicable language for knowledge representation, controlled vocabularies and references.Reusability – data and metadata and data should be well-described so that they can be replicated and/or combined in different settings, with clear data usage licenses, accurate description and clear provenance (Initiative).