1. CSE 6392 – Data Exploration and Analysis in Relational Databases April 20, 2006

2. Ranking Using Materialized View View – results of a query Materialized View – persistent results Two problems need to be solved: 1.Which views should be materialized? 2.Given a query, how do you best use the materialized views?

3. Ranking Query f: w 1 x 1 +w 2 x 2 +…+w m x m k (number of tuples) output: top-k tuples Possible ranking algorithms: -scan: only uses the base table -TA – uses “views” for sorted lists x1x1 xmxm t1t1 tntn

4. Ranking Query – Materialized Views In this new (not yet published) work, tackling the problem of using the materialized views rather than the traditional “skinny” tables Assume that we already have a bunch of materialized views corresponding to ranking queries: Ex. sorted k-tuples for functions (with materialized views): 3x 1 +2x 2 +5x 3 (Q1) 2x 1 +3x 2 (Q2) 2x 2 +4x 3 (Q3) If we get another query that matches one of these, can use the materialized views.

5. Ranking Query – An Early Idea However, suppose we get the following query: Q: 2x 1 +4x 2 +x 3 How do we solve this? An early idea: Ex. Q: 2x 1 +5x 2 +4x 3 Could do the TA algorithm on Q 2 + Q 3 Linear programming.

6. Ranking Query – Current Solution Geometric background. Suppose you have the following: Q 1 : 2x 1 +4x 2 +x 3, and k = 1 (top tuple) X Y Perpendicular line (3,2) iso-score line (every point on line has some score) Highest score is the best

7. Ranking Query – How Does This Actually Work? In original TA algorithm, the advantage is the stopping condition. In this approach, the stopping condition is when the linear programming solution drops below the threshold. This paper is not published yet.

8. Summary of Ranking 1)Fast execution of ranking queries/functions scan, TA, Lp TA inverted lists 2)Ranking function in IR vector space/TF-IDF probabilistic 3)Ranking on the web PageRank HITS 4)Ranking in databases keyword search (DBXplorer, Discover, Ranks) Probabilistic info retrieval