Analyzing data quality issues in research information systems via data profiling

Azeroual, Otmane; Saake, Gunter; Schallehn, Eike

doi:10.1016/j.ijinfomgt.2018.02.007

Cited by 57 publications

(34 citation statements)

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“…Step 3: Calculate the rough total influence relationship matrix T with Equation (11). The element ij t indicates the rough interdependent effects that criteria i has on criteria j, where I is an identity matrix.…”

Section: The Rdanp Methodsmentioning

confidence: 99%

“…Zheng et al [10] argued that information quality and system quality have a strong influence on individual benefits and user satisfaction in a virtual community. Azeroual et al [11] suggested that the success of a research information system is largely related to the quality of the available data, and they improved the quality of the research information system via data profiling. Dwivedi et al [12] summarized the factors that determine the success and failures of the information systems, and found that the type of information systems, such as enterprise resource planning (ERP) e-government, and the degree of implementation by employees and management are the main factors.…”

Section: Introductionmentioning

confidence: 99%

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A Rough Hybrid Multicriteria Decision-Making Model for Improving the Quality of a Research Information System

Shao¹,

Liou²,

Weng³

et al. 2019

Symmetry

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Improving the quality of research information systems is an important goal in the process of improving the performance of research management in Chinese universities. Since the evaluation of information system (IS) quality is a multicriteria decision problem, it is critical to identify the interrelationships among the dimensions and criteria, and decide on the important criteria for proposed improvement strategies. This paper suggests a hybrid multicriteria decision-making (MCDM) model for improving the quality of a research information system. First, a rough method combined with the decision-making trial and evaluation laboratory and analytical network process (rough DANP) model is used to improve the objectivity of expert judgements. Additionally, the rough DANP can be used to construct an influential network relationship map (INRM) between research information system components to derive the criterion weights. The complex proportional assessment of alternatives with rough numbers (COPRAS-R) is applied to evaluate the performance of the research information system. A Chinese university research information system is chosen to illustrate the usefulness of the proposed model. The results show that efficiency, effectiveness, and user frequency have the highest priorities for improvement. Selected management implications based on the actual case study are supplied.

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Section: The Rdanp Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Rough Hybrid Multicriteria Decision-Making Model for Improving the Quality of a Research Information System

Shao¹,

Liou²,

Weng³

et al. 2019

Symmetry

View full text Add to dashboard Cite

show abstract

“…The presentation layer (frontend) shows the target group-specific preparation and presentation of the analysis results for the user, which are made available in the form of reports using business intelligence tools, via portals, websites, etc. (for more details see the papers from [10][11][12][13]).…”

Section: Current Research Information Systemsmentioning

confidence: 99%

“…To monitor the quality of the data in CRIS, the following developed iterative process flow (see Figure 13) can be used as a basis for the institutions using CRIS and should serve as a guide to demonstrate how to analyze, detect, fix, and improve data quality issues in CRIS in the institutions. Figures 12 and 13, as iterative processes, provide a permanent assurance of data quality in current research information systems, as erroneous data in a collection are a fundamental challenge for managers and IT, quality assurance and many other fields (see also [9,12,13]). To accomplish the most difficult steps, such as analyzing and correcting data errors in the two processes, the data quality team should begin by agreeing on where the problems are greatest and the impact of the missing or erroneous data.…”

Section: Data Monitoringmentioning

confidence: 99%

“…However, a CRIS community in a given institution is more heterogeneous, made up of research managers, project leaders, data officers, administrative staff, librarians, people from the IT department, etc., and, last but not least, by the scientists themselves, with their own divisions (disciplines, infrastructures, early career scientists vs. tenured professors, etc.). Figures 12 and 13, as iterative processes, provide a permanent assurance of data quality in current research information systems, as erroneous data in a collection are a fundamental challenge for managers and IT, quality assurance and many other fields (see also [9,12,13]). To accomplish the most difficult steps, such as analyzing and correcting data errors in the two processes, the data quality team should begin by agreeing on where the problems are greatest and the impact of the missing or erroneous data.…”

Section: Data Monitoringmentioning

confidence: 99%

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Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

Azeroual

Schöpfel

2019

Publications

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Collecting, integrating, storing and analyzing data in a database system is nothing new in itself. To introduce a current research information system (CRIS) means that scientific institutions must provide the required information on their research activities and research results at a high quality. A one-time cleanup is not sufficient; data must be continuously curated and maintained. Some data errors (such as missing values, spelling errors, inaccurate data, incorrect formatting, inconsistencies, etc.) can be traced across different data sources and are difficult to find. Small mistakes can make data unusable, and corrupted data can have serious consequences. The sooner quality issues are identified and remedied, the better. For this reason, new techniques and methods of data cleansing and data monitoring are required to ensure data quality and its measurability in the long term. This paper examines data quality issues in current research information systems and introduces new techniques and methods of data cleansing and data monitoring with which organizations can guarantee the quality of their data.

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A Hadoop‐big data analytic model to predict and classify chronic kidney diseases using improved fractional rough fuzzy K‐means clustering and extreme gradient boost rat swarm optimizer

Vijayalakshmi

Ramaraj

2022

Concurrency and Computation

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In this article, a Hadoop-big data based chronic kidney disease prediction and classification using improved fractional rough fuzzy K-means (IF-RFKM) clustering and XG boost rat swarm optimizer is proposed. Here, IF-RFKM clustering method is contemplated for the disease prediction. This disease is classified using XG boost classifier for classifying the stages of chronic kidney diseases as normal and abnormal. Moreover, the rat swarm optimization (RSO) algorithm is proposed for optimizing the parameters of the XG boost classifier. Initially, the data is randomly generated from CKD dataset. The simulation is carried out in python language. From the simulation, the proposed method attains higher accuracy 99.57%, 98.28%, and 97.35%, higher recall 98.23%, 88.34%, and 78.96% and lower execution time 92.15%, 90.25%, and 92.48% compared with existing methods, like chronic kidney disease detection and classification by recursive feature elimination using decision tree (CCKD-RFE-DT). An efficient chronic kidney disease classification and clustering using logistic regression (CCKD-LR) and efficient chronic kidney disease classification utilizing multi-kernel support vector machine with (CCKD-MKC-SVM). K E Y W O R D Sbig data, chronic kidney diseases, improved fractional rough K means clustering, rat swarm optimization, XG boost classifier INTRODUCTIONNowadays, the technologies have developed with substantial changes that lead to the improvement of technologies with huge amounts of data. 1 If the data is not used, it becomes the amount of data that cannot be used. Thus, a quantity of unusable data can be used as a very valued data basis if they are appropriately administered using a method named data mining. 2 Data mining is used to discover stimulating outline from information concealed in a database. 3 By using machine learning techniques and statistical calculations, the discovery of pattern is prepared to construct models that prognosis the performance of data. 4 Classification is a technique for discovering patterns from data. A huge quantity of data are analyzed rapidly by data mining technique. 5 Actually, in the field of statistics, business, forecasting, and communication engineer's, data mining is not an advanced technique that utilizes patterns in data search for the purpose of prediction, verification, and identification. 6 Currently, many clinical databases have been developed based on the progress of the health database management system. By data mining techniques, composite databases are sustained easily. 7 Data mining

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Analyzing data quality issues in research information systems via data profiling

Cited by 57 publications

References 0 publications

A Rough Hybrid Multicriteria Decision-Making Model for Improving the Quality of a Research Information System

A Rough Hybrid Multicriteria Decision-Making Model for Improving the Quality of a Research Information System

Quality Issues of CRIS Data: An Exploratory Investigation with Universities from Twelve Countries

A Hadoop‐big data analytic model to predict and classify chronic kidney diseases using improved fractional rough fuzzy K‐means clustering and extreme gradient boost rat swarm optimizer

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