improving the data quality in the research information systems

1. Improving the Data
Quality in the Research
Information Systems
Presented by:
Nikil Sai Veerepaneni (45240)
Mahek Rehan (45118)
Jyoti (44150)

2. introduction
The research information systems provide the scientists with many
opportunities for collecting, categorizing and using the information.
 It is used for making publications, making the projects.
 The efforts for preparing the projects has been reduced for producing
reports.
Also utilized for the presentation of the research and scientific
expertise.
The quality of the information consists of more importance.

3. RESEARCH INFORMATION SYSTEM (RIS) –
ARCHITECTURE:
Generally, the RIS architecture contains:
The Data access layer consists of inner and outer information
sources. This phase consists of databases from the administration of
libraries checkers.
The Application layer consists of the research data and its
implementations that merge, organize and analyze the information
held at the low level.
In the Presentation Level, the aimed team-particular preparations and
representations of the checking results are sent to the user. The
different chances of reporting also fil the portals of the company.

4. PROBLEMS OF THE DATA QUALITY:
The given below are common quality problems in the content
of RIS
o Missing values.
o False data.
o Copied information on the dataset.
o Dissimilar presented information.
o Logically contradiction numbers.

5. IMPROVEMENT OF THE DATA QUALITY:
The method of recognizing and modifying the mistakes and
inconsistencies with the argot of improving the quality of the data
resources present in RIS is called as Data Cleansing.
The data cleaning method can be shown as:
• Explaining and finding the real issues.
• Find and recognizing the faulty information’s.
• Modifying the found mistakes.

6. IMPROVEMENT OF THE DATA QUALITY:
PARSING-The initial process of the data cleaning, which helps the client to
understand and modify the features more precisely.
CORRECTION AND STANDARDIZATION-It is next required to analyze the
parsed information for the correctness and then modify it next.
ENCHANTMENT OR DATA ENRICHMENT- It is the method that extends
the present information with data from other resources
DATA PROFILING-It is where there the information to be fine capture technical
shapes, analyzes them for the support if finding the information quality problems.
DATA AFTER CLEANING UP-Here the zipping code is concerned on the basis
of the addresses and joined a different area.

7. CONCLUSION
 The enhancement of the data quality can be processed at various
stages at data cleaning methods in any RIS and which data quality can
be gained.
 The view and advancement of the data quality are always aimed. It
provides considerable processes and use cases on one hand. Those data
errors should be corrected and enhanced by the data cleaning.
 A low-level control authority can rectify the software errors like
typing mistakes.
 Data cleaning tools are initially commercial and contained for both
little application contents and bigger data integration applications. The
demand for data cleaning is advancing as a service.

8. REFERENCES
[1] Batini, C. and Scannapieco, M. Data Quality - Concepts,
Methodologies and Techniques; Springer-Verlag, Heidelberg, 2006.
[2] Gebauer, M. and Windheuser, U. Structured Data Analysis, Profiling
and business rules, Springer Fachmedien Wiesbaden 2015.
[3] Helmis, S. and Hollmann, R. Web-based data integration,
approaches to measuring and securing the quality of information in
heterogeneous data sets using a fully web-based tool, 1. edition ©
Vieweg+Teubner | GWV Fachverlage GmbH, Wiesbaden 2009.