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Supporting Ranking in Queries Score-based Paradigm Russell Greenspan CS 411 Spring 2004
Supporting Ranking in Queries Talk Outline ,[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking in Queries What is ranking in queries? ,[object Object],[object Object],[object Object],[object Object],[object Object]
What is ranking in queries? Definitions ,[object Object],[object Object],[object Object],[object Object]
What is ranking in queries? Differences from traditional queries ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking in Queries Why use ranking in queries? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Ranking in Queries How do we use execute ranked queries? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
How do we use execute ranked queries? “Smart” Ranked Query Execution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Smart” Ranked Query Execution Two Areas of Research Focus ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Smart” Ranked Query Execution Research and Techniques ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Smart” Ranked Querying (Middleware) – Garlic [Fagin, 1999] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Garlic [Fagin, 1999] Rank Processing Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Garlic [Fagin, 1999] Example Query ,[object Object],.4 H .7 G .1 F .9 E .2 D .3 C .8 B .6 A Roundness Object .3 H .9 G .5 F .3 E .8 D .1 C .6 B .2 A Redness Object
Garlic [Fagin, 1999] Inefficient vs. Efficient Processing ,[object Object],[object Object],[object Object],[object Object],[object Object],.12 H .63 G .05 F .27 E .16 D .03 C .48 B .12 A Score Object
Garlic [Fagin, 1999] Inefficient vs. Efficient Processing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Garlic [Fagin, 1999] Conclusions ,[object Object],[object Object],[object Object]
“ Smart” Ranked Querying (Middleware) – CHITRA [Nepal, Ramakrishna, 1999] ,[object Object],[object Object]
CHITRA [Nepal, Ramakrishna, 1999] “Multi-step” Algorithm ,[object Object],[object Object],[object Object],[object Object]
CHITRA [Nepal, Ramakrishna, 1999] Example Query ,[object Object],.4 H .7 G .1 F .9 E .2 D .3 C .8 B .6 A Roundness Object .3 H .9 G .5 F .3 E .8 D .1 C .6 B .2 A Redness Object
CHITRA [Nepal, Ramakrishna, 1999] Example Scoring Functions Results ,[object Object],[object Object],[object Object],B = min[.6, .8] = .6 G = min[.7, .9] = .7 D = min[.8, .2] = .2 B = min[.8, .6] = .6 G = min[.9, .7] = .7 E = min[.9, .3] = .3 Grade {G, B} min[.6, .7] = .6 i1 = {B(.6)} i2 = {G(.7)} 3 min[.8, .8] = .8 i1 = {D(.8)} i2 = {B(.8)} 2 min[.9, .9] = .9 i1 = {G(.9)} i2 = {E(.9)} 1 Resultset Threshold Items Iter. B = [.6 * .8] = .48 G = [.7 * .9] = .63 D = [.8 * .2] = .16 B = [.8 * .6] = .48 G = [.9 * .7] = .63 E = [.9 * .3] = .27 Grade {G, B} [.6 * .7] = .43 i1 = {B(.6)} i2 = {G(.7)} 3 [.8 * .8] = .64 i1 = {D(.8)} i2 = {B(.8)} 2 [.9 * .9] = .81 i1 = {G(.9)} i2 = {E(.9)} 1 Resultset Threshold Items Iter.
CHITRA [Nepal, Ramakrishna, 1999] Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Smart” Ranked Querying (Relational) – STOP Operator [Carey, et al, 1997] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
STOP Operator [Carey, et al, 1997] Example query plans ,[object Object],[object Object],[object Object],[object Object]
STOP Operator [Carey, et al, 1997] Conservative Heuristic ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
STOP Operator [Carey, et al, 1997] Aggressive Heuristic ,[object Object],[object Object],[object Object]
STOP Operator [Carey, et al, 1997] Experimental Results ,[object Object],[object Object],[object Object],18.5 sec 63.1 sec 63.9 sec 128.3 sec Aggressive, Overestimate (10) Aggressive, Underestimate (1/10) Conservative Traditional
STOP Operator [Carey, et al, 1997] Experimental Results ,[object Object]
“ Smart” Ranked Querying (Relational) – Probabilistic [Donjerkovic, et al, 1999] ,[object Object],[object Object],[object Object]
Probabilistic [Donjerkovic, et al, 1999] Comparison with STOP Operator ,[object Object]
Probabilistic [Donjerkovic, et al, 1999] Implementation ,[object Object],[object Object],[object Object]
Probabilistic [Donjerkovic, et al, 1999] Performance ,[object Object],[object Object]
“ Smart” Ranked Querying (Relational) – Statistical [Chaudhuri, Gravano, 1999] ,[object Object],[object Object],[object Object]
Statistical [Chaudhuri, Gravano, 1999] Expansion of probabilistic model ,[object Object],[object Object],[object Object],[object Object]
Statistical [Chaudhuri, Gravano, 1999] Implementation ,[object Object],[object Object]
Statistical [Chaudhuri, Gravano, 1999] Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object]
Statistical [Chaudhuri, Gravano, 1999] Expansion of Fagin’s model ,[object Object],[object Object],[object Object]
“ Smart” Ranked Querying (Rank) – MPro [Chang, Hwang, 2002] ,[object Object],[object Object],[object Object],[object Object]
MPro [Chang, Hwang, 2002] Determining if probe is necessary ,[object Object],[object Object],[object Object],[object Object],[object Object]
MPro [Chang, Hwang, 2002] Determining all necessary probes ,[object Object],[object Object],[object Object]
MPro [Chang, Hwang, 2002] Minimal Probes Algorithm (MPro) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MPro [Chang, Hwang, 2002] Further Applications ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
MPro [Chang, Hwang, 2002] Experimental Results ,[object Object],[object Object]
“ Smart” Ranked Querying (Rank) – AutoRank [Agrawal, et al, 2003] ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AutoRank [Agrawal, et al, 2003] IDF Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AutoRank [Agrawal, et al, 2003] Q F Similarity ,[object Object],[object Object],[object Object],[object Object],[object Object]
AutoRank [Agrawal, et al, 2003] Implementation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
AutoRank [Agrawal, et al, 2003] Experimental Results ,[object Object],[object Object],[object Object],[object Object]
Conclusions ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Future ,[object Object],[object Object],[object Object]
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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probabilistic ranking

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