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1 – 10 of over 111000
Article
Publication date: 14 July 2023

Hamid Hassani, Azadeh Mohebi, M.J. Ershadi and Ammar Jalalimanesh

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video…

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Abstract

Purpose

The purpose of this research is to provide a framework in which new data quality dimensions are defined. The new dimensions provide new metrics for the assessment of lecture video indexing. As lecture video indexing involves various steps, the proposed framework containing new dimensions, introduces new integrated approach for evaluating an indexing method or algorithm from the beginning to the end.

Design/methodology/approach

The emphasis in this study is on the fifth step of design science research methodology (DSRM), known as evaluation. That is, the methods that are developed in the field of lecture video indexing as an artifact, should be evaluated from different aspects. In this research, nine dimensions of data quality including accuracy, value-added, relevancy, completeness, appropriate amount of data, concise, consistency, interpretability and accessibility have been redefined based on previous studies and nominal group technique (NGT).

Findings

The proposed dimensions are implemented as new metrics to evaluate a newly developed lecture video indexing algorithm, LVTIA and numerical values have been obtained based on the proposed definitions for each dimension. In addition, the new dimensions are compared with each other in terms of various aspects. The comparison shows that each dimension that is used for assessing lecture video indexing, is able to reflect a different weakness or strength of an indexing method or algorithm.

Originality/value

Despite development of different methods for indexing lecture videos, the issue of data quality and its various dimensions have not been studied. Since data with low quality can affect the process of scientific lecture video indexing, the issue of data quality in this process requires special attention.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Article
Publication date: 25 September 2009

Anders Haug, Jan Stentoft Arlbjørn and Anne Pedersen

In literature, there is not agreement on the relevant data quality dimensions in an enterprise resource planning (ERP) system context. The purpose of this paper is to provide some…

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Abstract

Purpose

In literature, there is not agreement on the relevant data quality dimensions in an enterprise resource planning (ERP) system context. The purpose of this paper is to provide some clarification of this topic, by answering two important questions: What are the most relevant dimensions for assessing ERP data quality? What are the causal relationships between these data quality dimensions?

Design/methodology/approach

Based on a discussion of existing literature on data quality, a classification model of ERP system data quality is proposed and the relationships between the defined categories of data quality dimensions are defined. The validity of the classification model and the relationships between categories of data quality dimensions are investigated in three case studies.

Findings

The three case studies confirm that the classification model captures the most important aspects of describing ERP data quality and that the defined causalities between categories of data quality dimensions correspond with practice.

Research limitations/implications

Besides being relevant in an ERP system context, the contribution of this paper may also be applicable for the evaluation of data quality in other types of information systems.

Practical implications

The defined classification model of ERP system data quality may support companies in improving their ERP data quality, thereby achieving greater benefits from their ERP systems.

Originality/value

A clarification of the most important data quality aspects in an ERP context is provided. Furthermore, some of the most important causalities between categories of data quality are defined.

Details

Industrial Management & Data Systems, vol. 109 no. 8
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 17 January 2019

Saikat Deb and Mokaddes Ali Ahmed

The purpose of this paper is to estimate and compare the service quality of the city bus service measured by two different approaches which are subjective service quality

Abstract

Purpose

The purpose of this paper is to estimate and compare the service quality of the city bus service measured by two different approaches which are subjective service quality dimensions and objective service quality dimensions.

Design/methodology/approach

The objective service quality dimensions have been estimated based on the benchmarking technique provided by the Ministry of Urban Development, India. For the analysis of subjective service quality dimensions, a questionnaire survey has been conducted to measure the users’ satisfaction and dissatisfaction about the service. The questionnaire consists of users’ socioeconomic characteristics and 23 questions related to city bus service quality dimensions. Questionnaire data have been analyzed by factor analysis, regression analysis and path analysis to find out the indicators representing subjective service quality dimensions. Finally, the overall service quality of the bus service has been determined based on both the measures.

Findings

The study indicates that the overall service quality of the bus service is different for subjective and objective analyses. While the objective measures show that the service quality is very good, the subjective measures indicate that the service is not doing well.

Research limitations/implications

The analysis of the subjective dimensions is complicated. Analysis of the subjective dimensions needed more expertise and resources than the objective analysis.

Originality/value

In this study, the estimated service quality of the bus service is more reliable than the other methods as it comprises of both operators’ perspective and passengers’ expectations from the service.

Details

Benchmarking: An International Journal, vol. 26 no. 2
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 12 January 2015

Hong Huang

– The purpose of this paper is to understand genomics scientists’ perceptions in data quality assurances based on their domain knowledge.

Abstract

Purpose

The purpose of this paper is to understand genomics scientists’ perceptions in data quality assurances based on their domain knowledge.

Design/methodology/approach

The study used a survey method to collect responses from 149 genomics scientists grouped by domain knowledge. They ranked the top-five quality criteria based on hypothetical curation scenarios. The results were compared using χ2 test.

Findings

Scientists with domain knowledge of biology, bioinformatics, and computational science did not reach a consensus in ranking data quality criteria. Findings showed that biologists cared more about curated data that can be concise and traceable. They were also concerned about skills dealing with information overloading. Computational scientists on the other hand value making curation understandable. They paid more attention to the specific skills for data wrangling.

Originality/value

This study takes a new approach in comparing the data quality perceptions for scientists across different domains of knowledge. Few studies have been able to synthesize models to interpret data quality perception across domains. The findings may help develop data quality assurance policies, training seminars, and maximize the efficiency of genome data management.

Details

Journal of Documentation, vol. 71 no. 1
Type: Research Article
ISSN: 0022-0418

Keywords

Article
Publication date: 4 April 2016

Mahdi Zahedi Nooghabi and Akram Fathian Dastgerdi

One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators for…

Abstract

Purpose

One of the most important categories in linked open data (LOD) quality models is “data accessibility.” The purpose of this paper is to propose some metrics and indicators for assessing data accessibility in LOD and the semantic web context.

Design/methodology/approach

In this paper, at first the authors consider some data quality and LOD quality models to review proposed subcategories for data accessibility dimension in related texts. Then, based on goal question metric (GQM) approach, the authors specify the project goals, main issues and some questions. Finally, the authors propose some metrics for assessing the data accessibility in the context of the semantic web.

Findings

Based on GQM approach, the authors determined three main issues for data accessibility, including data availability, data performance, and data security policy. Then the authors created four main questions related to these issues. As a conclusion, the authors proposed 27 metrics for measuring these questions.

Originality/value

Nowadays, one of the main challenges regarding data quality is the lack of agreement on widespread quality metrics and practical instruments for evaluating quality. Accessibility is an important aspect of data quality. However, few researches have been done to provide metrics and indicators for assessing data accessibility in the context of the semantic web. So, in this research, the authors consider the data accessibility dimension and propose a comparatively comprehensive set of metrics.

Details

Program, vol. 50 no. 2
Type: Research Article
ISSN: 0033-0337

Keywords

Article
Publication date: 3 November 2022

Reza Edris Abadi, Mohammad Javad Ershadi and Seyed Taghi Akhavan Niaki

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of…

Abstract

Purpose

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Design/methodology/approach

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

Findings

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

Research limitations/implications

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Originality/value

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

Details

Information Discovery and Delivery, vol. 51 no. 4
Type: Research Article
ISSN: 2398-6247

Keywords

Article
Publication date: 3 August 2021

Pratima Verma, Vimal Kumar, Ankesh Mittal, Bhawana Rathore, Ajay Jha and Muhammad Sabbir Rahman

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis…

Abstract

Purpose

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

Design/methodology/approach

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

Findings

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

Research limitations/implications

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Originality/value

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

Details

The TQM Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 24 August 2021

Anders Haug

Numerous data quality (DQ) definitions in the form of sets of DQ dimensions are found in the literature. The great differences across such DQ classifications (DQCs) imply a lack…

Abstract

Purpose

Numerous data quality (DQ) definitions in the form of sets of DQ dimensions are found in the literature. The great differences across such DQ classifications (DQCs) imply a lack of clarity about what DQ is. For an improved foundation for future research, this paper aims to clarify the ways in which DQCs differ and provide guidelines for dealing with this variance.

Design/methodology/approach

A literature review identifies DQCs in conference and journal articles, which are analyzed to reveal the types of differences across these. On this basis, guidelines for future research are developed.

Findings

The literature review found 110 unique DQCs in journals and conference articles. The analysis of these articles identified seven distinct types of differences across DQCs. This gave rise to the development of seven guidelines for future DQ research.

Research limitations/implications

By identifying differences across DQCs and providing a set of guidelines, this paper may promote that future research, to a greater extent, will converge around common understandings of DQ.

Practical implications

Awareness of the identified types of differences across DQCs may support managers when planning and conducting DQ improvement projects.

Originality/value

The literature review did not identify articles, which, based on systematic searches, identify and analyze existing DQCs. Thus, this paper provides new knowledge on the variance across DQCs, as well as guidelines for addressing this.

Details

Industrial Management & Data Systems, vol. 121 no. 12
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 15 February 2023

Charalampos Alexopoulos, Stuti Saxena, Nina Rizun and Deo Shao

This research paper aims to present a framework of open government data (OGD) relating to e-service quality dimensions. In addition, it provides a research agenda for the…

Abstract

Purpose

This research paper aims to present a framework of open government data (OGD) relating to e-service quality dimensions. In addition, it provides a research agenda for the e-service delivery of OGD.

Design/methodology/approach

A literature review pertaining to e-service quality with special reference to e-government was delivered to deduce the key dimensions of e-service quality for OGD.

Findings

Five e-service quality dimensions of OGD are identified in the study; website design, fulfilment, service provision to the user while interfacing with the OGD Web portal, service provision to the user during and after the value-creation and innovation period and security/privacy. To further OGD re-use for value creation and innovation, it is important that the e-service quality dimensions are built into all OGD programmes by public authorities.

Originality/value

Hitherto, extant research has focused on the data quality dimensions of OGD, but the dimensions linked with e-service have not been explored. This study seeks to fill this gap and, in addition, suggests further research requirements in this field.

Details

Records Management Journal, vol. 33 no. 1
Type: Research Article
ISSN: 0956-5698

Keywords

Article
Publication date: 3 February 2023

Huyen Nguyen, Haihua Chen, Jiangping Chen, Kate Kargozari and Junhua Ding

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

Abstract

Purpose

This study aims to evaluate a method of building a biomedical knowledge graph (KG).

Design/methodology/approach

This research first constructs a COVID-19 KG on the COVID-19 Open Research Data Set, covering information over six categories (i.e. disease, drug, gene, species, therapy and symptom). The construction used open-source tools to extract entities, relations and triples. Then, the COVID-19 KG is evaluated on three data-quality dimensions: correctness, relatedness and comprehensiveness, using a semiautomatic approach. Finally, this study assesses the application of the KG by building a question answering (Q&A) system. Five queries regarding COVID-19 genomes, symptoms, transmissions and therapeutics were submitted to the system and the results were analyzed.

Findings

With current extraction tools, the quality of the KG is moderate and difficult to improve, unless more efforts are made to improve the tools for entity extraction, relation extraction and others. This study finds that comprehensiveness and relatedness positively correlate with the data size. Furthermore, the results indicate the performances of the Q&A systems built on the larger-scale KGs are better than the smaller ones for most queries, proving the importance of relatedness and comprehensiveness to ensure the usefulness of the KG.

Originality/value

The KG construction process, data-quality-based and application-based evaluations discussed in this paper provide valuable references for KG researchers and practitioners to build high-quality domain-specific knowledge discovery systems.

Details

Information Discovery and Delivery, vol. 51 no. 4
Type: Research Article
ISSN: 2398-6247

Keywords

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