Azeroual, Otmane

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Azeroual, Otmane

ORCID

0000-0002-5225-389X

Now showing 1 - 9 of 9

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Improving the Data Quality in the Research Information Systems
(IJCSIS, 2017-11-30)
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In order to introduce an integrated research information system, this will provide scientific institutions with the necessary information on research activities and research results in assured quality. Since data collection, duplication, missing values, incorrect formatting, inconsistencies, etc. can arise in the collection of research data in different research information systems, which can have a wide range of negative effects on data quality, the subject of data quality should be treated with better results. This paper examines the data quality problems in research information systems and presents the new techniques that enable organizations to improve their quality of research data.
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A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information
(The Canadian Center of Science and Education, 2019-10-30)
With the increased accessibility of research information, the demands on research information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of information causes problems. If the data is structured in RIS, users can read and filter out their information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of research information and assigning them to specific topics. Research information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured research information from heterogeneous information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured research information.
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Data Quality Measures and Data Cleansing for Research Information Systems
(Digital Information Research Foundation, 2018-02-01)
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The collection, transfer and integration of research information into different research information systems can result in different data errors that can have a variety of negative effects on data quality. In order to detect errors at an early stage and treat them efficiently, it is necessary to determine the clean-up measures and the new techniques of data cleansing for quality improvement in research institutions. Thereby an adequate and reliable basis for decision-making using an RIS is provided, and confidence in a given dataset increased.
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Research Intelligence (CRIS) and the Cloud: A Review
(The Canadian Center of Science and Education, 2019-09-30)
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The purpose of this paper is to explore the impact of the cloud technology on current research information systems (CRIS). Based on an overview of published literature and on empirical evidence from surveys, the paper presents main characteristics, delivery models, service levels and general benefits of cloud computing. The second part assesses how the cloud computing challenges the research information management, from three angles: networking, specific benefits, and the ingestion of data in the cloud. The third part describes three aspects of the implementation of current research systems in the clouds, i.e. service models, requirements and potential risks and barriers. The paper concludes with some perspectives for future work. The paper is written for CRIS administrators and users, in order to improve research information management and to contribute to future development and implementation of these systems, but also for scholars and students who want to have detailed knowledge on this topic.
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Solving problems of research information heterogeneity during integration – using the European CERIF and German RCD standards as examples
(IOS Press, 2019-09-18)
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Integrating data from a variety of heterogeneous internal and external data sources (e.g. CERIF and RCD data models with different modeling languages) in a federated database system such as “Research Information Management System (RIMS)” is becoming more challenging for (inter-)national universities and research institutions. Data quality is an important factor for successful integration and interpretation of research information and interoperability of various independent information systems. Before the data is loaded into RIMS, they should be reviewed during data integration process to resolve conflicts between the different data sources and clean the data quality issues. Poor data quality leads to distortion in data presentation, and thus to erroneous basis for decisions. It is ultimately a cost for scientific institutions and it starts with integrating research information into the RIMS. Therefore, the investment in the topic of information integration makes sense insofar, the achievement of a high data quality is of primary importance. This paper presents methods, processes and techniques of information integration in the context of research information management systems. In order to ensure the quality of research information in an institutions data sources during its integration into the RIMS. Numerous attempts have already been done by universities and research institutions to create techniques and solutions for this need.
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Text data mining and data quality management for research information systems in the context of open data and open science
(École des Sciences de l’Information (ESI, Rabat, Maroc), 2018-11-28)
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In the implementation and use of research information systems (RIS) in scientific institutions, text data mining and semantic technologies are a key technology for the meaningful use of large amounts of data. It is not the collection of data that is difficult, but the further processing and integration of the data in RIS. Data is usually not uniformly formatted and structured, such as texts and tables that cannot be linked. These include various source systems with their different data formats such as project and publication databases, CERIF and RCD data model, etc.
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Quality issues of CRIS data: an exploratory investigation with universities from twelve countries
(MDPI, 2019-02-22)
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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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Quality Issues of CRIS Data
(euroCRIS, 2018-06-15)
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In recent years, current research information systems have become an integral part of university IT landscapes, and in this regard, their importance in research has greatly increased. Through the use of CRIS, scientific institutions can provide a current overview of their research activities, collect, process and manage information about their scientific activities, projects and output as well as integrate them into their web presence. Furthermore, CRIS can contribute to rationalize and optimize academic research activities, through highly efficient procedures and added value information. In the context of the scientific Big Data, research and development studies often focus on the “3 Vs”, i.e. volume, velocity and variety. Our paper addresses the 4th and 5th Vs, ie data variability (inconsistency), and data veracity (quality) as a specific problem for research information systems. Here, we distinguish between the quality of the system and the quality of the content, in particular of the data input.
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Quality of Research Information in RIS Databases: A Multidimensional Approach
(Springer Nature, 2019-05-18)
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For the permanent establishment and use of a RIS in universities and academic institutions, it is absolutely necessary to ensure the quality of the research information, so that the stakeholders of the science system can make an adequate and reliable basis for decision-making. However, to assess and improve data quality in RIS, it must be possible to measure them and effectively distinguish between valid and invalid research information. Because research information is very diverse and occurs in a variety of formats and contexts, it is often difficult to define what data quality is. In the context of this present paper, the data quality of RIS or rather their influence on user acceptance will be examined as well as objective quality dimensions (correctness, completeness, consistency and timeliness) to identify possible data quality deficits in RIS. Based on a quantitative survey of RIS users, a reliable and valid framework for the four relevant quality dimensions will be developed in the context of RIS to allow for the enhancement of research information driven decision support.

Azeroual, Otmane

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