Probabilistic Ranking Techniques in Relational Databases

Ihab F. Ilyas is an Associate Professor of Computer Science at the University of Waterloo. He received his PhD in computer science from Purdue University, West Lafayette, in 2004. He holds BS and MS degrees in computer science from Alexandria University, Egypt. His main research is in the area of database systems, with special interest in top-k and rank-aware query processing, managing uncertain and probabilistic databases, self-managing databases, indexing techniques, and spatial databases. Mohamed A. Soliman is a software engineer at Greenplum, where he works on building massively distributed database systems for efficient support of data warehousing and analytics. He received his PhD in computer science from University of Waterloo in 2010. He holds BS and MS degrees in computer science from Alexandria University, Egypt. His main research is in the area of rank-aware retrieval in relational databases, focusing primarily on supporting ranking queries on uncertain and probabilistic data.

Ranking queries are widely used in data exploration, data analysis and decision making scenarios. While most of the currently proposed ranking techniques focus on deterministic data, several emerging applications involve data that are imprecise or uncertain. Ranking uncertain data raises new challenges in query semantics and processing, making conventional methods inapplicable. Furthermore, the interplay between ranking and uncertainty models introduces new dimensions for ordering query results that do not exist in the traditional settings. This lecture describes new formulations and processing techniques for ranking queries on uncertain data. The formulations are based on marriage of traditional ranking semantics with possible worlds semantics under widely-adopted uncertainty models. In particular, we focus on discussing the impact of tuple-level and attribute-level uncertainty on the semantics and processing techniques of ranking queries. Under the tuple-level uncertainty model, we describe new processing techniques leveraging the capabilities of relational database systems to recognize and handle data uncertainty in score-based ranking. Under the attribute-level uncertainty model, we describe new probabilistic ranking models and a set of query evaluation algorithms, including sampling-based techniques. We also discuss supporting rank join queries on uncertain data, and we show how to extend current rank join methods to handle uncertainty in scoring attributes. Table of Contents: Introduction / Uncertainty Models / Query Semantics / Methodologies / Uncertain Rank Join / Conclusion

Introduction.- Uncertainty Models.- Query Semantics.- Methodologies.- Uncertain Rank Join.- Conclusion.