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تعداد صفحات این فایل: ۲۴ صفحه

بخشی از مقاله انگلیسیعنوان انگلیسی:Analyzing data quality issues in research information systems via data profiling~~en~~

Abstract

The success or failure of a RIS in a scientific institution is largely related to the quality of the data available as a basis for the RIS applications. The most beautiful Business Intelligence (BI) tools (reporting, etc.) are worthless when displaying incorrect, incomplete, or inconsistent data. An integral part of every RIS is thus the integration of data from the operative systems. Before starting the integration process (ETL) of a source system, a rich analysis of source data is required. With the support of a data quality check, causes of quality problems can usually be detected. Corresponding analyzes are performed with data profiling to provide a good picture of the state of the data. In this paper, methods of data profiling are presented in order to gain an overview of the quality of the data in the source systems before their integration into the RIS. With the help of data profiling, the scientific institutions can not only evaluate their research information and provide information about their quality, but also examine the dependencies and redundancies between data fields and better correct them within their RIS.

Introduction

In recent years, the requirements for research reporting of all institutions have risen massively. Both internal institution (presidium, departmental and specialist management) and external (relevant ministries, interested public) need comprehensive information on the research activities of the institutions for planning and control purposes. Research activities include research information such as Projects, thirdparty funds, patents, partners, prices and publications, etc. This research information is stored and managed in a corresponding research information system. A research information system is used when the information system from different sources brings together research information via an integrated ETL process and makes it possible to analyze it using various output and analysis functions. With the introduction of RIS, scientific institutions can provide a current overview of their research activities, collect, process and manage information about their scientific activities, projects and publications as well as integrate them into their web presence. Furthermore, RIS users can not have any extra effort in the survey of their research activities.

The growing volumes of data and the increasing number of source systems can lead to possible data errors, duplicates, missing values, incorrect formatting and contradictions in RIS. In addition, it serves to analyze the quality of the data in the source systems prior to their integration into the RIS. The sooner quality defects are controlled and remedied, the better. For this purpose, the aim of this paper will be to present the possible methods of data profiling applicable to research information in order to allow facilities to carry out a detailed analysis and evaluation on the existing data.

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