scholarly journals An infrastructure with user-centered presentation data model for integrated management of materials data and services

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Shilong Liu ◽  
Yanjing Su ◽  
Haiqing Yin ◽  
Dawei Zhang ◽  
Jie He ◽  
...  

AbstractWith scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative, the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community. In this paper, we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability. We call this architecture the Materials Genome Engineering Databases (MGED). MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers, unify data representation forms with user-centered presentation data model, and accelerate data discovery with advanced search capabilities. MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data. We describe MGED’s design, current status, and how MGED supports integrated management of shared data and services.

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.


Author(s):  
William J. Rasdorf ◽  
Lisa K. Spainhour

Abstract Researchers and materials engineers require a greater understanding of the problems and solutions that emerge when integrating composite materials data with computer technology so that utilitarian composite materials databases can be developed to effectively and efficiently support analysis and design software. Composite materials constitute a representational challenge due to their composition and use. However, this paper suggests that a conceptual composite material data model and application software interfaces must be developed to support the dissemination and use of composite materials data. This paper primarily serves to analyze several of the problems facing developers of composite materials databases, evolving from the complexity of the materials themselves and from the current lack of testing and data representation standards. Without a clear understanding of the scope and nature of these problems, there is no possibility of designing concise yet comprehensive composites data models, yet we feel that such an understanding is presently lacking. In addition, an effort is made to present possible solutions to these difficulties being suggested and/or implemented both by the authors and by other researchers in the field. Such an effort provides a firm foundation upon which future research may be based.


2021 ◽  
Vol 64 (1) ◽  
pp. 57-65
Author(s):  
Young Hwan Kim ◽  
Hong Rim Cha ◽  
Ji Eun Lee ◽  
Se Eun Cha ◽  
Yeong Jin Choi

Human-derived materials are a crucial element of research in the life sciences. The Korea Biobank Network (KBN) portal is a shared open platform that provides the nationʼs most extensive disease resources, possessed by Human Bioresource Unit Banks of the KBN, to the public, including those in the fields of industry, academia, and research. This platform was developed to increase the efficient use of national disease resources. In the KBN portal, the current status of disease resources collected in Korea can be checked online. Human bioresources and clinical information are provided to consumers through systematic search and efficient distribution programs. Additionally, by simultaneously operating the KBN Distribution Support Center, we are working to support the rapid and convenient distribution of human resources in response to the needs of consumers. To effectively utilize the open human bioresource sharing platform, it is necessary to introduce an integrated clinical information management system. Currently, the KBN is in the process of establishing standard terminology for data and applying a common data model for the integrated management of various clinical information held by the KBN. We provide communications through the KBN portal, which is interconnected with the distribution support center, regional biobanks, and consumers. In conclusion, the KBN portal will provide open innovation by creating a business or service model by delivering shared open data and internalizing external innovative capabilities.


2019 ◽  
Vol 8 (3) ◽  
pp. 7753-7758

The article presents an adaptable data model based on multidimensional space. The main difference between a multidimensional data representation and a table representation used in relational Database Management Systems (DBMSs) is that it is possible to add new elements to sets defining the axes of multidimensional space at any time. This changes the data model. The tabular representation of the relational model does not allow you to change the model itself during the operation of an automated system. Three levels of multidimensional data presentation space are considered. There are axis of multidimensional space, the Cartesian product of the sets of axis values and the values of space points. The five axes of multidimensional space defined in the article (entities, attributes, identifiers, time, modifiers) are basic for the design of an adaptable automated system. It is shown that it is possible to use additional axes for greater granularity of the stored data. The multidimensional space structure defined in the article for an adaptable data model is a flexible set for storing a relational domain model. Two types of operations in multidimensional information space are defined. Relations of the relational model are formed dynamically depending on the conditions imposed on the coordinates of the points. Thus, an adaptable data representation model based on multidimensional space can be used to create flexible dynamic automated information systems.


Data Mining ◽  
2013 ◽  
pp. 1-27
Author(s):  
Sangeetha Kutty ◽  
Richi Nayak ◽  
Tien Tran

With the increasing number of XML documents in varied domains, it has become essential to identify ways of finding interesting information from these documents. Data mining techniques can be used to derive this interesting information. However, mining of XML documents is impacted by the data model used in data representation due to the semi-structured nature of these documents. In this chapter, we present an overview of the various models of XML documents representations, how these models are used for mining, and some of the issues and challenges inherent in these models. In addition, this chapter also provides some insights into the future data models of XML documents for effectively capturing its two important features, structure and content, for mining.


2012 ◽  
Vol 55 (5) ◽  
pp. 994-1007 ◽  
Author(s):  
Seunghoon Lee ◽  
Soonhung Han ◽  
Duhwan Mun

2016 ◽  
Vol 25 (01) ◽  
pp. 178-183 ◽  
Author(s):  
M. Barros ◽  
F.M. Couto

Summary Introduction: Biomedical research is increasingly becoming a data-intensive science in several areas, where prodigious amounts of data is being generated that has to be stored, integrated, shared and analyzed. In an effort to improve the accessibility of data and knowledge, the Linked Data initiative proposed a well-defined set of recommendations for exposing, sharing and integrating data, information and knowledge, using semantic web technologies. Objective: The main goal of this paper is to identify the current status and future trends of knowledge representation and management in Life and Health Sciences, mostly with regard to linked data technologies. Methods: We selected three prominent linked data studies, namely Bio2RDF, Open PHACTS and EBI RDF platform, and selected 14 studies published after 2014 (inclusive) that cited any of the three studies. We manually analyzed these 14 papers in relation to how they use linked data techniques. Results: The analyses show a tendency to use linked data techniques in Life and Health Sciences, and even if some studies do not follow all of the recommendations, many of them already represent and manage their knowledge using RDF and biomedical ontologies. Conclusion: These insights from RDF and biomedical ontologies are having a strong impact on how knowledge is generated from biomedical data, by making data elements increasingly connected and by providing a better description of their semantics. As health institutes become more data centric, we believe that the adoption of linked data techniques will continue to grow and be an effective solution to knowledge representation and management.


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