Efficient Join Processing over Uncertain Data - By Reynold Cheng, et all.

1. Efficient Join Processing over Uncertain Data - By Reynold Cheng, et all. Presented By Lydia & Usha

2. Main Idea The main key point addressed in this paper is extending traditional join techniques to DBs with uncertain attributes. Improving the efficiency of join based algorithms to address some of the complexities faced by DB management in handling joins over uncertain data. Efficient pruning techniques involving both uncertainty interval and uncertainty pdf.

3. Importance of the problem • Often spatial DB applications have to deal with uncertain data. For example • GPS data which calculates nearest neighbour • Sensor data from realtime applications • Scientific data corresponding to weather.

4. Key concepts Addresses the semantic complexities of uncertain data Using probabilistic threshold joins. Define uncertainty comparison operators & probabilistic join queries. Proposes novel techniques like page and index level joins which incorporates efficient pruning techniques and evaluating the performance of those algorithmsover uncertain data.

6. Probabilistic uncertainty model Uncertainty interval & uncertainty pdf.

7. Item Level Join

8. Page level pruning

9. Page level pruning

10. Index level Join Used to improve IO throughput When combined with node level pruning technique, this improves performance as well as IO throughput. In this pages are organized in an ordered tree structure, allowing one to use MBR which tightly encloses the interval within the subtree.

11. Experimental Result.