LaTeX

UVA SEAS Thesis

A LaTeX package adhering to the formatting guidelines of the University of Virginia’s School of Engineering and Applied Science.

1.2.54 (17 March 2012) | Documentation | Examples: print and electronic

UVA Beamer Theme

A Beamer presentation theme for the University of Virginia’s School of Engineering and Applied Science.

0.9.44 (7 September 2011)

Benchmark

Download data sets, topics, and relevance assessments (qrels):

Data Set Database Topics and Assessments
Mondial schema and data topics and qrels
IMDb schema and data topics and qrels
Wikipedia schema and data topics and qrels
IMDb (complete) schema and data topics and qrels
Wikipedia (complete) schema and data topics and qrels

This data may only be used for academic (personal and non-commercial) work and must not be resold or repurposed.

The IMDb information was obtained using IMDbPY and is courtesy of

The Internet Movie Database
http://www.imdb.com
Used with permission

Please also see IMDb’s copyright notice and terms of data use.

Datasets are dumped from PostgreSQL. Some commands may not be supported by other relational database management systems. To reduce time when loading the data, it is recommended that the schema file be split so indexes are created after loading the data.

The “complete” data sets denote the original data set instead of the subset used for evaluation.

Additional evaluation revealed minor mistakes with the original IMDb qrels. To facilitate comparison with published evaluations, the original qrels remain available above. Corrected qrels: IMDb

Please reference the following publication when using these data sets, topics, or relevance assessments:

Joel Coffman and Alfred C. Weaver, “A Framework for Evaluating Database Keyword Search Strategies,” Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM ‘10), pp. 729–738, Toronto, Canada, pp. 729–738, October 2010

Further empirical evaluation is available in later work:

Joel Coffman and Alfred C. Weaver, “An Empirical Performance Evaluation of Relational Keyword Search Techniques,” IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 26, No. 1, pp. 30–42, January 2014