Visual-aided ontology-based ranking on multidimensional data: a case study in academia

Triperina, Evangelia; Bardis, Georgios; Sgouropoulou, Cleo; Xydas, Ioannis; Terraz, Olivier; Miaoulis, Georgios

doi:10.1108/dta-03-2017-0014

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…However, there is also a need for collaboration among the different sections of libraries for the implementation of Big Data analytics. As the research of Triperina et al (2018) reveals that the used ontology in the academia provides the obviousness of classification, feminizing's to new solicitations, semantic web relationships and deepens of data mining. This process leads to the implementation process of Big Data analytics in organizations.…”

Section: Literature Reviewmentioning

confidence: 99%

An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries

Ahmad

Zheng

Rafi

2019

DTA

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Purpose The purpose of this paper is to analyze the views and capabilities of librarians for the implementation of Big Data analytics in academic libraries of Pakistan. The study also sets out to check the relationship between the required skills of librarians and the application of Big Data analytics. Design/methodology/approach A survey was conducted to gather the required data from the targeted audience. The targeted population of the study was Head/In charge library managers of Pakistani university libraries, which were 173 in total. All the respondents (academic librarians) were invited through an e-mail to respond to the survey voluntarily. Out of 173 respondents from higher education commission of Pakistan chartered university libraries, 118 librarians (68.2 percent) completed the survey that was finally considered, and after checking data, recommendation for analysis was made. To analyze the collected data, statistical technique Pearson correlation was applied using statistical package for social science version 25 to know the strength of the mutual correlation of variables. Findings The findings of the study show a strong correlation between the required competencies and skills of librarians for the implementation of Big Data analytics in academic libraries. In all variables of the study, the correlation was highly significant, except two of the variables, including “concept of Big Data” and “different forms of data.” The study also reveals that most of the respondents were well aware of the concept of Big Data analytics. Moreover, they were using a large amount of data to carry out various library operations, including the acquisition, preservation, curation and analysis of data. Originality/value This study is significant in the sense that it fills a substantial gap in the literature regarding the perspective of librarians on Big Data analytics.

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Section: Literature Reviewmentioning

confidence: 99%

An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries

Ahmad

Zheng

Rafi

2019

DTA

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“…This again can be interpreted as a multicriteria decision-making (MCDM) problem with alternatives as the set of DMUs and criteria as the set of different cross-efficiency formulations. In the last decade, MCDM is widely used approach and found to be well suited for application domains that are ranked on the basis of multiple criteria (Triperina et al , 2018; Ma, 2019).…”

Section: Introductionmentioning

confidence: 99%

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

Puri

Verma

2020

DTA

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PurposeThis paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.Design/methodology/approachSelf-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.FindingsThe proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.Research limitations/implicationsThe choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.Practical implicationsTo prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.Originality/valueTo the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

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“…According to algorithm 1, the resolution method is called again and the SDR for this criterion is withdrawn, leaving the dimension with the SDR for the rest indicators of the dimension. A fragment of the set of SDR for "Internationalization" dimension can be described as follows [294]:…”

Section: Sdr Rules Have Been Developed For the Indicators Of Each Dim...mentioning

confidence: 99%

Visual interactive knowledge management for multicriteria decision making and ranking in linked open data environments

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Η διδακτορική διατριβή αφορά στην εφαρμογή της οπτικής αναλυτικής και των τεχνολογιών σημασιολογικού ιστού στην πολυκριτηριακή λήψη αποφάσεων και πιο συγκεκριμένα σε προβλήματα κατάταξης εναλλακτικών, στο πλαίσιο των ερευνητικών και ακαδημαϊκών πληροφοριακών συστημάτων. Οι οπτικοποιήσεις υποστηρίζονται επίσης από διαδικασίες εξόρυξης δεδομένων και εξαγωγής γνώσης σε περιβάλλοντα διασυνδεδεμένων δεδομένων. Επιπροσθέτως, θα χρησιμοποιηθούν τεχνικές οπτικής αναλυτικής για την οργάνωση των οπτικοποιήσεων, προκειμένου να παρουσιαστούν οι πληροφορίες με τέτοιο τρόπο που θα αξιοποιηθούν οι αντιληπτικές ικανότητες των χρηστών και τελικά θα ενισχύσουν τις διαδικασίες υποστήριξης αποφάσεων και χάραξης πολιτικής. Επιπλέον, η οπτική αναπαράσταση και κατά συνέπεια οι διαδικασίες λήψης αποφάσεων και χάραξης πολιτικής θα βελτιωθούν μέσω των σημασιολογικών τεχνολογιών που βασίζονται σε εννοιολογικά μοντέλα με τη μορφή οντολογιών. Έτσι, ο κύριος στόχος της παρούσας διδακτορικής διατριβής είναι ο συνδυασμός των σημασιολογικών τεχνολογιών με τεχνικές διαδραστικών οπτικοποιήσεων, προκειμένου να γίνουν πιο αποτελεσματικά τα συστήματα υποστήριξης αποφάσεων. Κατά την διάρκεια του διδακτορικού αναπτύχθηκε ένα πολυδιάστατο πλαίσιο βασισμένο σε οντολογίες για οπτική κατάταξη (MOBVR framework), το οποίο το εφαρμόστηκε σε δυο μελέτες περίπτωσης, η πρώτη ήταν ο ακαδημαϊκός τομέας και η δεύτερη ήταν οι δείκτες παγκόσμιας ανάπτυξης.

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Visual-aided ontology-based ranking on multidimensional data: a case study in academia

Cited by 5 publications

References 30 publications

An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries

An analysis of academic librarians competencies and skills for implementation of Big Data analytics in libraries

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

Visual interactive knowledge management for multicriteria decision making and ranking in linked open data environments

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