Abstract
To provide scientific institutions with comprehensive and well-maintained documentation of their research information in a current research information system (CRIS), they have the best prerequisites for the implementation of text and data mining (TDM) methods. Using TDM helps to better identify and eliminate errors, improve the process, develop the business, and make informed decisions. In addition, TDM increases understanding of the data and its context. This not only improves the quality of the data itself, but also the institution’s handling of the data and consequently the analyses. This present paper deploys TDM in CRIS to analyze, quantify, and correct the unstructured data and its quality issues. Bad data leads to increased costs or wrong decisions. Ensuring high data quality is an essential requirement when creating a CRIS project. User acceptance in a CRIS depends, among other things, on data quality. Not only is the objective data quality the decisive criterion, but also the subjective quality that the individual user assigns to the data.
Highlights
The steady growth of data and especially of textual data in the constantly expanding organizational environment leads to the necessity to integrate text and data mining (TDM) into current research information systems (CRIS)
TDM is versatile and plays a major role in the research management area, which is mainly confronted with research information in text form
Finding such research information is inefficient and time consuming over traditional search engines
Summary
Different research institutions use research information for different purposes. Data analyses and reports based on current research information systems (CRIS) provide information about the research activities and their results. Research activities and their results at universities and academic institutions have been collected, maintained, and published via CRIS in a variety of forms and heterogeneous data sources [5]. Unstructured data presents a major challenge for CRIS administrators, especially for universities and academic institutions that manage their research information from heterogeneous data sources in CRIS [5]. The methods of TDM by means of statistical and linguistic analysis methods aim at the detection of hidden and interesting information or patterns in unstructured text documents, on the one hand to be able to process the huge amount of words and structures of the natural language, and on the other hand to allow the treatment of uncertain and fuzzy data.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
