Overview

Many data models and query languages (or interfaces) have been proposed, and enhancements or modifications continue to be proposed, adding to the variety. However, there have been very few empirical studies of how users actually performed with these data models and interfaces. Empirical studies are important for differentiating good and bad concepts from the perspectives of users. Knowledge arising from such studies can be applied in the design of training courses for users, in the communication processes between users and database personnel, and in the design or modification of data models and interfaces.

Area of Focus

1. User retrieval performance.

   Factors affecting user performance that have been studied include data model (relational, entity-relationship, object-oriented), query language (SQL, KQL, OQL), abstraction level (physical, logical, conceptual), query complexity (simple, complex), query format (text, visual), and query syntax (formal, natural). Besides providing statistical results on user performance, the studies also reported error analyses.

   Chan, H.C., Tan, B.C.Y. and Wei, K.K. "Three Important Determinants of User Performance for Database Retrieval," International Journal of Human-Computer Studies, 1999, Volume 51, Number 5, pp. 895-918.

   Chan, H.C., Siau, K.L. and Wei, K.K. "The Effect of Data Model, System, and Task Characteristics on User Query Performance: An Empirical Study," Data Base, 1998, Volume 29, Number 1, pp. 31-49.

   Siau, K.L., Chan, H.C. and Wei, K.K. "The Effects of Conceptual and Logical Interfaces on Visual Query Performance of End-Users," Proceedings of the Sixteenth Annual International Conference on Information Systems, 1995, pp. 225-235.

   Wu, C.Z., Chan, H.C., Teo, H.H. and Wei, K.K. "An Experimental Study of Object-Oriented Query Language and Relational Query Language for Novice Users," Journal of Database Management, 1994, Volume 5, Number 4, pp. 16-27.

   Chan, H.C., Wei, K.K. and Siau, K.L. "User-Database Interface: The Effect of Abstraction Levels on Query Performance," MIS Quarterly, 1993, Volume 17, Number 4, pp. 441-464.

2. User update performance.

   As for retrieval, update performance can be affected by many factors. The study examines the impact of data model, query language, and abstraction level.

   Chan, H.C., Wei, K.K. and Siau, K.L. "An Empirical Study on End Users’ Update Performance for Different Abstraction Levels," International Journal of Human-Computer Studies, 1994, Volume 41, Number 4, pp. 309-328.

3. Query usage in real-life.

   The focus is to understand usage of queries in real-life applications in organizations. Results can aid design of training courses by highlighting error-prone concepts.

   Lu, H.J., Chan, H.C. and Wei, K.K. "A Survey on Usage of SQL," ACM SIGMOD Record, 1993, Volume 22, Number 4, pp. 60-65.

4. Query feedback.

   Feedback may impact user performance. Simple feedback can point out syntax errors, such as missing commas or wrong keywords. Advance feedback includes suggestions for correcting the queries and even English translations of the query. Advance feedback is studied for its impact on user retrieval performance.

   Chan, H.C., Wei, K.K. and Siau, K.L. "The Effect of a Database Feedback System on User Performance," Behavior and Information Technology, 1995, Volume 14, Number 3, pp. 152-162.

5. Summary studies.

   Often, past research provides seemingly contradictory results. Meta-analysis is used to summarize and help provide a more coherent picture.

   Chan, H.C. and Lim, L.H. "Database Interfaces: A Conceptual Framework and a Meta-Analysis on Natural Languages Studies," Journal of Database Management, 1998, Volume 9, Number 3, pp. 25-32.

6. Complexity.

   In addition to experimental comparisons, studies are made on providing theoretical backings for quantifying complexity in user-database interaction.

   
   Chan, H.C. "The Relationship between User Query Accuracy and Lines of Code," International Journal of Human-Computer Studies, 1999, Volume 51, Number 5, pp. 851-864.