Data Warehouse with Big Data Technology for Higher Education

Santoso, Leo Willyanto; Yulia, Yulia

doi:10.1016/j.procs.2017.12.134

Cited by 96 publications

(50 citation statements)

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“…In this section, the approach proposed in Section 4 is applied to the case of a University, wanting to upgrade its ranking in the NIRF ranking of India [35]. This would also help in devising an improved website of the University for a better web presence, more distinguished placements and refinement of the decision‐making process for the introduction of new courses [56]. For this purpose, the said University intends to compare and contrast itself with those Universities/Institutions in North India which are ranked above the said University in the NIRF ranking.…”

Section: Case Studymentioning

confidence: 99%

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

2020

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The incorporation of suitable external data from the World Wide Web offers an effective solution for enriching the data in the data warehouse (DW). However, the main challenge is the quality‐aware selection of web data sources to maintain the quality of the DW. In the previous works, the quality evaluation of web sources is through expert evaluation only, which makes it a very lengthy process. Also, since the quality model consists of mixed quality factors from diverse domains of Web, DW and underlying business, finding an expert possessing an expertise of all these domains is a huge bottleneck in the evaluation process. In order to overcome these existing issues, this study proposes a novel multi‐level approach web source evaluation with multi‐criteria decision‐making and web quality testing tools (WSEMQT) and underlying quality model web quality model for evaluating web sources for the DW. The authors introduce automated web source quality evaluation in the first level of web source based evaluation and multiple dimensions of quality evaluation at the second level of expert‐based evaluation. At both the levels, multi‐criteria decision‐making methods are applied to the evaluation scores obtained to ascertain the ranked list of Web sources. The authors present a real‐world academic web data case study which shows that the proposed approach can be executed successfully for real‐world problems.

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Section: Case Studymentioning

confidence: 99%

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

2020

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“…But developing countries are also gaining new technologies. Using these technologies enables storing, managing, and analyzing large datasets with maintaining security [77]. Also, it provides relevant data on class activities that are recorded using high-definition cameras and video clips that help evaluate student facial expressions and can track their movements as well as make decisions for organizations [78].…”

Section: Educationmentioning

confidence: 99%

Big Data: Analytical Solutions, Research Challenges and Trends

Ali¹,

Novikov²

2020

Proceedings of ISP RAS

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The term Big Data refers to an extensive collections of digital data generating every second. Produced datasets come in structured, semi-structured, and unstructured formats throughout the world, which is difficult for the traditional database management systems to analyze. Recently, big data analytics emerges as an essential research area due to the popularity of the Internet and the advent of new Web technologies. This growing area of research represents a multidisciplinary that attracts researchers from various research fields. Interested researchers are invited to design, develop, and implement several tools, technologies, architecture, and platforms for analyzing these large volumes of data. This paper begins with a brief introduction to big data and related concepts, including the main characteristics of big data, followed by discussions of the most significant open research challenges and emerging trends. Next, we review a study of big data analytics, the advantages of using big data solutions, and the preliminary assessments required before migrating from traditional solutions. Finally, we present a review of the recent main applications to obtain a broad perspective of big data analytics.

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“…The evaluation of the important attributes that may affect student performance could improve the quality of the higher education system [11,12,13]. Radaideh, Al-Sahwakf and Al-Najjar [14] presented a classification model by implementing ID3 and C4.5 algorithms of the decision tree techniques and the Naive Bayes.…”

Section: Literature Reviewmentioning

confidence: 99%

The Analysis of Student Performance Using Data Mining

Santoso

Yulia

2019

Advances in Intelligent Systems and Computing

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The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia.The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and world-wide distribution. This permits a rapid and broad dissemination of research results. ** Indexing: The books of this series are submitted to ISI Proceedings, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink ** More information about this series at

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Data Warehouse with Big Data Technology for Higher Education

Cited by 96 publications

References 3 publications

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

Big Data: Analytical Solutions, Research Challenges and Trends

The Analysis of Student Performance Using Data Mining

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Data Warehouse with Big Data Technology for Higher Education

Cited by 96 publications

References 3 publications

WSEM QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

WSEM QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

Big Data: Analytical Solutions, Research Challenges and Trends

The Analysis of Student Performance Using Data Mining

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Product

Resources

About

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse

WSEM _QT : a novel approach for quality‐based evaluation of web data sources for a data warehouse