Top-k Query Processing in Uncertain Database

1. Top-k Query Processing  in Uncertain Database Mohamed A. Soliman, Ihab F. Ilyas, Kevin Chen-Chuan Chang. ICDE07 Kai, Jiang Fudan University

2. Outline Introduction Processing Framework U-Topk Queries U-kRanks Queries Queries with Tuple Independence Experiments Conclusion

3. Introduction Uncertain (probabilistic) data sensor networks, moving objects tracking, data cleaning etc. Uncertain data model Possible worlds: a set of possible instances Confidence: membership uncertainty Generation rules: logical formulas determine valid worlds Independent: tuples correlated with no rules

4. Uncertain Database

5. Motivation & Challenges Different from traditional top-k queries Not depend only on score function but also on membership probability Two interesting top-k queries Top-k speeding cars in the last hour A ranking over the models of the top-k speeding cars Interaction between �most probable� and �top-k�: several different possible interpretations Involve both ranking and aggregation across worlds which is prohibitively expensive

6. Problem Definition: U-Topk Uncertain Top-k Query (U-Topk): Let D be an uncertain database with possible worlds space PW={PW1, . . . , PWn}. Let T={T1, . . . , Tm} be a set of k-length tuple vectors, where for each Ti?T: (1)Tuples of Ti are ordered according to scoring function F (2) Ti is the top-k answer for a non empty set of possible worlds . A U-Topk query, based on F, returns T*?T, where

7. Problem Definition: U-kRanks Uncertain k Ranks Query (U-kRanks): Let D be an uncertain database with possible worlds space PW={PW1, . . . , PWn}. For i=1�k, let be a set of tuples, where each tuple appears at rank i in a non empty set of possible worlds based on scoring function F. A U-kRanks query, based on F, returns , where