EAN: 9783031007187

SYNTHESIS LECTURES ON DATA MANAGEMENT PROBABI...

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

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