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Data Quality Assessment for NoSQL Databases: Wide-column stores – Cassandra

Supervisor: a.Univ.-Prof. DI Dr. Wolfram Wöß
Co-Supervisor: DI Lisa Ehrlinger, BSc

Motivation and Challenges

Data quality assessment is crucial to estimate the significance of data analysis results and decisions that are based on these results. The primary information source for personal decisions is the Internet, whereas large enterprises usually process their data from several historically developed and heterogeneous information systems. In both cases, prior to decision-making, an integration of the available data is necessary to compare and assess the content of single sources.

A Java-based system has been developed at our institute that analyzes different information sources and calculates metrics to estimate a system's data quality. Currently, it is possible to assess and compare MySQL databases, CSV files and ontological schema descriptions. Schema heterogeneity is resolved by transforming different information source schemas into a unified form using the DSD ("Data Source Description") vocabulary. However, until now, an in-depth investigation of the most widely applied NoSQL databases is missing.


The main objective of this master's thesis is to evaluate quality assessment of wide column stores on the basis of a practical implementation using Apache Cassandra. Cassandra is a NoSQL database optimized for scalability and high availability, with tunable consistency as trade-off. The practical work includes (1) the transformation of a Cassandra DB schema to a DSD representation in order to achieve comparability, and (2) an evaluation of the implementation by comparing a Cassandra schema to relational data (e.g., a MySQL DB).

The following research questions should be answered in the course of this thesis:

  • Is it possible to achieve direct comparability in terms of DQ assessment between wide column stores and relational data?
  • If not, what are the major obstacles and how could they be addressed?
  • Are there existing approaches for assessing the data quality specifically in column stores, and if so, how do they differ from traditional approaches (i.e., relational data)?