Integrated access to big data polystores through a knowledge-driven framework

McHugh, Justin; Cuddihy, Paul; Williams, Jenny Weisenberg; Aggour, Kareem S.; Kumar, V. S. Anil; Mulwad, Varish

doi:10.1109/bigdata.2017.8258083

Cited by 33 publications

(14 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data considered depends on the type of question asked; this clarification is important to determine if it is structured or unstructured data. The extracted data are processed and analyzed by the big data Hadoop framework and the processing includes the cleaning of the data that guarantees the quality of the results [53,54]. The processed data are analyzed by means of various big data tools that make comparisons, cross information, projections, etc.…”

Section: Integration Of a Big Data Model In Adaptive Learningmentioning

confidence: 99%

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

et al. 2020

View full text Add to dashboard Cite

Currently, data are a very valuable resource for organizations. Through analysis, it is possible to profile people or obtain knowledge about an event or environment and make decisions that help improve their quality of life. This concept takes on greater value in the current pandemic, due to coronavirus disease 2019 (COVID-19), that affects society. This emergency has changed the way people live. As a result, the majority of activities are carried out using the internet, virtually or online. Education is not far behind and has seen the web as the most successful option to continue with its activities. The use of any computer application generates a large volume of data that can be analyzed by a big data architecture in order to obtain knowledge from its students and use it to improve educational processes. The big data, when included as a tool for adaptive learning, allow the analysis of a large volume of data to offer an educational model based on personalized education. In this work, the analysis of educational data through a big data architecture is proposed to generate learning based on meeting the needs of students.

show abstract

Section: Integration Of a Big Data Model In Adaptive Learningmentioning

confidence: 99%

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Previous research has allowed us to identify different works aligned with the use of big data in controlled environments, focusing specifically on the use of Hadoop as an analysis architecture [17,18]. The research carried out is divided into two groups, and the first provides a perspective on the application of big data in Hadoop architecture [19,20]. Much of this architecture focuses on processes that do not necessarily look for results in real time; however, it needs high availability and great processing capacity [21].…”

Section: Previous Researchmentioning

confidence: 99%

“…These needs range from improving administrative processes to analyzing the data of the inhabitants [23]. In this group, there was little research into the architecture or integral analysis where all the data generated in a campus are taken advantage of through a process of big data analysis [20] to help in efficiently managing processes and resources [24].…”

Section: Previous Researchmentioning

confidence: 99%

Application of a Big Data Framework for Data Monitoring on a Smart Campus

et al. 2019

View full text Add to dashboard Cite

At present, university campuses integrate technologies such as the internet of things, cloud computing, and big data, among others, which provide support to the campus to improve their resource management processes and learning models. Integrating these technologies into a centralized environment allows for the creation of a controlled environment and, subsequently, an intelligent environment. These environments are ideal for generating new management methods that can solve problems of global interest, such as resource consumption. The integration of new technologies also allows for the focusing of its efforts on improving the quality of life of its inhabitants. However, the comfort and benefits of technology must be developed in a sustainable environment where there is harmony between people and nature. For this, it is necessary to improve the energy consumption of the smart campus, which is possible by constantly monitoring and analyzing the data to detect any anomaly in the system. This work integrates a big data framework capable of analyzing the data, regardless of its format, providing effective and efficient responses to each process. The method developed is generic, which allows for its application to be adequate in addressing the needs of any smart campus.

show abstract

“…General Electric (GE) also proposed a semantic-driven framework SemTK, which realized storage and access of heterogeneous big data. In addition, the benefits of constructing and querying Polystore knowledge graphs with SemTK were employed in four industrial cases [39].…”

Section: Industrial Dataspacementioning

confidence: 99%

Mini-MES: A Microservices-Based Apps System for Data Interconnecting and Production Controlling in Decentralized Manufacturing

Jiang

Liu

2019

Applied Sciences

View full text Add to dashboard Cite

Manufacturing factories and their resources are moving towards being networked, flat, flexible, and diversified in the context of social manufacturing. It is urgent to manage the increasingly complex and variable manufacturing processes effectively in such cross-factory production. This paper proposes a brand-new microservices-based Mini-MES (M2ES) Apps System, which adopts the decentralized and distributed architecture to comply with the needs of software supported manufacturing. The basic properties of the M2ES Apps System, such as its running logic and its data organization, are discussed, and a real-world industrial case is described to illustrate how it works. Results show that the proposed M2ES Apps System has better performance, reliability, and scalability than the initial information systems.

show abstract

Integrated access to big data polystores through a knowledge-driven framework

Cited by 33 publications

References 21 publications

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Data Analysis as a Tool for the Application of Adaptive Learning in a University Environment

Application of a Big Data Framework for Data Monitoring on a Smart Campus

Mini-MES: A Microservices-Based Apps System for Data Interconnecting and Production Controlling in Decentralized Manufacturing

Contact Info

Product

Resources

About