answering top-k queries using views updated
TRANSCRIPT
-
8/12/2019 Answering Top-k Queries Using Views Updated
1/64
Answering Top-k Queries Using
Views
By:
Gautam Das (Univ. of Texas),
Dimitrios Gunopulos (Univ. of California Riverside),
Nick Koudas (Univ. of Toronto),Dimitris Tsirogiannis (Univ. of Toronto)
Presented By:
Kushal Shah
Lipsa Patel
-
8/12/2019 Answering Top-k Queries Using Views Updated
2/64
Views
Definition: Views
Declaring Views
Advantages of using Views
-
8/12/2019 Answering Top-k Queries Using Views Updated
3/64
Views
A viewmay be thought of as a table, that is derived
from one or more underlying base table.
Two kinds:
1. Virtual: Not stored in the database; just a
query for constructing the relation.2. Materialized: Actually constructed and
stored.
-
8/12/2019 Answering Top-k Queries Using Views Updated
4/64
Declaring Views
Materialized:
CREATE [MATERIALIZED]
VIEW AS ;
Virtual: Default
-
8/12/2019 Answering Top-k Queries Using Views Updated
5/64
Advantages of using Views
If we have several tables in a DB and we want to
view only specific columnsfrom specific tables we
can go for views.
Suffice the needs of security: Sometimes allowing
specific users to see only specific columns based onthe permission that we can configure on the views.
-
8/12/2019 Answering Top-k Queries Using Views Updated
6/64
Answering Top-k Queries Using
Views
By:
Gautam Das (Univ. of Texas),
Dimitrios Gunopulos (Univ. of California Riverside),
Nick Koudas (Univ. of Toronto),Dimitris Tsirogiannis (Univ. of Toronto)
Presented By:
Kushal Shah
Lipsa Patel
-
8/12/2019 Answering Top-k Queries Using Views Updated
7/64
Top-k Query
Top-k Query ProcessingDefinition
Top-k Example
Algorithms for Top-k Query Processing
-
8/12/2019 Answering Top-k Queries Using Views Updated
8/64
Top-k Query Processing
Top-k query processing
=Finding k objects that have the highest overall
Score
-
8/12/2019 Answering Top-k Queries Using Views Updated
9/64
Top-k Example
R
Users preferences regarding the ordering of the tuples of a
relation can be expressed as a scoring functions on theattributes of a relation, eg
fq = 3x1 + 2x2 + 5x3
The top-k problem is to find the k tuples with the highest
scoreaccording to a given scoring function.
tid X1 X2 X3
1 82 1 59
2 53 19 83
3 29 99 15
4 80 45 8
5 28 32 39
fQ
tid Score
2 612
1 543
4 370
3 360
5 343
-
8/12/2019 Answering Top-k Queries Using Views Updated
10/64
Algorithms for Top-k Query Processing
How? Which algorithms?Related Work How wecomplement existing approaches?
TA [Fagin]
PREFER [Hristidis]Stores the multiple copies of a relation and eachcopy is ordered according to a different scoringfunction.
In order to answer a top-k query the algorithmutilizes a single copy with a scoring function whichis closest to the scoring function of the query.
-
8/12/2019 Answering Top-k Queries Using Views Updated
11/64
(a, 0.9)
(b, 0.8)
(c, 0.72)
(d, 0.6)
.
.
.
.
Sorted L1
(d, 0.9)
(a, 0.85)
(b, 0.7)
(c, 0.2)
.
.
.
.
N
a
b
c
d
.
.
.
.
Object
ID
0.9
0.8
0.72
0.6
.
.
.
.
Attribute 1
0.85
0.2
0.9
.
.
.
.
Attribute 2
0.7
M
Sorted L2
ExampleSimple Database model
-
8/12/2019 Answering Top-k Queries Using Views Updated
12/64
ID A1 A2 Min(A1,A2)
Step 1: - parallel sorted access to each list
(a, 0.9)
(b, 0.8)
(c, 0.72)
(d, 0.6)
.
..
.
L1 L2
(d, 0.9)
(a, 0.85)
(b, 0.7)
(c, 0.2)
.
..
.
a
d
0.9
0.9
0.85 0.85
0.6 0.6
For each object seen:- get all grades by random access
- determine Min(A1,A2)
- amongst 2 highest seen ? keep in buffer
ExampleThreshold Algorithm
-
8/12/2019 Answering Top-k Queries Using Views Updated
13/64
ID A1 A2 Min(A1,A2)
a: 0.9
b: 0.8
c: 0.72
d: 0.6
.
..
.
L1 L2
d: 0.9
a: 0.85
b: 0.7
c: 0.2
.
..
.
Step 2: - Determine threshold value based on objects currently
seen under sorted access. T = min(L1, L2)
a
d
0.9
0.9
0.85 0.85
0.6 0.6
T = min(0.9, 0.9) = 0.9
- 2 objects with overall grade threshold value ? stop
else go to next entry position in sorted list and repeat step 1
ExampleThreshold Algorithm
-
8/12/2019 Answering Top-k Queries Using Views Updated
14/64
ID A1
A2
Min(A1
,A2
)
Step 1 (Again): - parallel sorted access to each list
(a, 0.9)
(b, 0.8)
(c, 0.72)
(d, 0.6)
.
..
.
L1 L2
(d, 0.9)
(a, 0.85)
(b, 0.7)
(c, 0.2)
.
..
.
a
d
0.9
0.9
0.85 0.85
0.6 0.6
For each object seen:
- get all grades by random access
- determine Min(A1,A2)
- amongst 2 highest seen ? keep in buffer
b 0.8 0.7 0.7
ExampleThreshold Algorithm
-
8/12/2019 Answering Top-k Queries Using Views Updated
15/64
ID A1 A2 Min(A1,A2)
a: 0.9
b: 0.8
c: 0.72
d: 0.6
.
..
.
L1 L2
d: 0.9
a: 0.85
b: 0.7
c: 0.2
.
..
.
Step 2 (Again): - Determine threshold value based on objects currently
seen. T = min(L1, L2)
a
b
0.9
0.7
0.85 0.85
0.8 0.7
T = min(0.8, 0.85) = 0.8
- 2 objects with overall grade threshold value ? stopelse go to next entry position in sorted list and repeat step 1
ExampleThreshold Algorithm
-
8/12/2019 Answering Top-k Queries Using Views Updated
16/64
c
ID A1 A2 Min(A1,A2)
a: 0.9
b: 0.8
c: 0.72
d: 0.6
.
..
.
L1 L2
d: 0.9
a: 0.85
b: 0.7
c: 0.2
.
..
.
Situation at stopping condition
a
b
0.9
0.7
0.85 0.85
0.8 0.7
T = min(0.72, 0.7) = 0.7
ExampleThreshold Algorithm
0.72 0.2 0.2
-
8/12/2019 Answering Top-k Queries Using Views Updated
17/64
Related Work for Top-k Query Processing
TA: Sequential as well as Random Access
PREFER
-
8/12/2019 Answering Top-k Queries Using Views Updated
18/64
Approach for Top-k Query Processing
Top-k Query Answering using Views
Views are Materialized(incurring space overhead)
Advantages of using views: increased performance
because views are small in size
Space-Performance tradeoff
-
8/12/2019 Answering Top-k Queries Using Views Updated
19/64
Example Views
R tid X1 X2 X3
1 82 1 59
2 53 19 83
3 29 99 15
4 80 45 8
5 28 32 39
Three attribute relation R
V1 tid Score
3 553
4 385
5 216
2 201
1 169
Top-5 queryusing
function f1 = 2x1 + 5x2
V2 tid Score
2 351
1 237
5 177
3 159
4 88
Top-5 queryusing function
f2 = x2 + 2x3
Top-k ranking queries in SQL-like syntax: SELECT TOP[k] FROM R ORDER BY Score(q)Score(q) - function that assigns numeric score to any tuple t
Ranking Views: Views only aim to rankA ranking view is the materialized result of a previously asked top-k query.
Can we answer new top-k queries efficiently using ranking
views? Lets see
-
8/12/2019 Answering Top-k Queries Using Views Updated
20/64
Formal Definitions
Ranking Queries
Ranking Views
-
8/12/2019 Answering Top-k Queries Using Views Updated
21/64
Ranking Queries
Ranking Queries: Top-k ranking queries in SQL-likesyntax: Select Top[k] from R where Range(q) Order By
Score(q)
A ranking query may be expressed as a triple Q = (Score(q),
k, Range(q)), where
Score(q)= Function that assigns numeric score to any tuple t
Range(q) = defines selection condition for the tuples of R Semantics: Retrieve the k tuples with the top scores
satisfying the selection condition.
-
8/12/2019 Answering Top-k Queries Using Views Updated
22/64
Ranking Views
Materialized Ranking View V:
for a previously executed query
Q1= (ScoreQ1, k1, RangeQ1),the corresponding materialized ranking view is a set of
k(tid, scoreQ(tid)) pairs,
ordered by decreasing values of scoreQ(tid).
-
8/12/2019 Answering Top-k Queries Using Views Updated
23/64
Problems we are going to solve
Top-k Query Answer using Views
View Selection
-
8/12/2019 Answering Top-k Queries Using Views Updated
24/64
Top-k Query Answer using Views
Given: Set Uof views
Query Q
Obtain an answer to Q combining all the information
conveyed by the views in U
Solution: Algorithm named LPTA
-
8/12/2019 Answering Top-k Queries Using Views Updated
25/64
Problems we are going to solve
Top-k Query Answer using Views
View Selection
-
8/12/2019 Answering Top-k Queries Using Views Updated
26/64
View Selection
Problem: Given a collection of views V={V1Vr} base
views and a query Q, determine the most efficientsubset U
of V to execute Q on.
Input to LPTA: subset U
Obtaining an answer to ranking query: Running TA on base
views.
Find the subset U that when utilized by LPTA1. Provide answer to query
2. Provide answer faster than running TA on the base
views V
-
8/12/2019 Answering Top-k Queries Using Views Updated
27/64
Outline
LPTA Algorithm
View Selection Problem
LPTA Li P i Ad i
-
8/12/2019 Answering Top-k Queries Using Views Updated
28/64
LPTA: Linear Programming Adaptation
of the Threshold Algorithm
1. Scoring function of Query: Q - fQ= 3x1 + 10x2
2. Scoring function of Views: V1fv1= 2x1 + 5x2
Subset of Views U V2fv2
= x1 + 2x2
LPTA for Top-k Query Answer using Views
Top-1 Query
View is a set of pairs of (tuple identifier, score).
The LPTA algorithm requires sorted access on each view in
non-increasing order of that score.
-
8/12/2019 Answering Top-k Queries Using Views Updated
29/64
LPTA Example
tid x1 x2 x3
1 82 1 59
2 53 19 83
3 29 1 2
4 80 22 90
5 28 8 87
6 12 55 827 16 99 42
8 18 42 67
9 42 1 23
10 23 21 88
Rtid Score
7 527
6 299
4 270
8 246
2 201
V1
Top-5 Queryf1 = 2x1 + 5x2
tid Score
6 219
4 202
10 197
Top-3 Query
f2 = x2 + 2x3
V2
Answer Top-2 Query using LPTA
-
8/12/2019 Answering Top-k Queries Using Views Updated
30/64
LPTA Setting
The algorithm initializes the top-k buffer to empty.
Top-2 Buffertid Score
7 527
6 299
4 270
8 246
2 201
tid Score
6 219
4 202
10 197
V1 V2
7 16 99 42
For each tid read, random access
on R to retrieve tuple and
compute score acc to query
function f3 = 3x1 + 10x2 + 5x3
6 12 55 82 (7,1248)
(6,996)
Top-2 Buffer
Check for stopping Condition
-
8/12/2019 Answering Top-k Queries Using Views Updated
31/64
Check for Stopping Condition
The unseen tuples in the view have satisfy the following inequalities:The domain of each attribute of R [1,100]
0
-
8/12/2019 Answering Top-k Queries Using Views Updated
32/64
Calculating Unseenmax
Unseenmax= Solution to the linear program where we maximize the
function f3 = 3x1 + 10x2 + 5x3 subject to these inequalities.
A linear programming problem may be defined as the problem of
maximizing or minimizing a linear function subject to linear
constraints. The constraints may be equalities or inequalities. Here is
a simple example.
Find numbersx1 andx2 that maximize the sumx1 +x2 subject to the
constraints
x1 0,x2 0, and
x1 + 2x2 4
4x1 + 2x2 12
x1 +x2 1
Objective Function
-
8/12/2019 Answering Top-k Queries Using Views Updated
33/64
Maximize the function
Convex region
This system of inequalities defines a
convex region.
Occasionally, the maximum occursalong an entire edge or face of the
constraint set, but then the maximum
occurs at a corner point as well.
LPTA E l
-
8/12/2019 Answering Top-k Queries Using Views Updated
34/64
LPTA - Example
tid11
s1
1
tid21
tid31
tid4
1
tid51
s21
s31
s41
s51
tid1
2
s12
tid2
2
tid3
2
tid4
2
tid5
2
s22
s32
s42
s52
V1 V2 tid11
tid1
2
Top-1 queryV1
V2
Qstopping
condition
X1
X2
R(X1, X2)
O(0,0)
P(1,0)
R (1,1)
T (0,1)
Normalized Domain[0,1]
Views and top-k query represented by
vectors denoting the direction of increasingscore
Sweeping line perpendicular
to V1 from infinity to origin
Score of a tuple with respect to the query: project that tuple to the vector of the query
Score of a tuple with respect to a view: project that tuple to the the vector of the view
Max posssible score of any tuple not yet
visited in the views with respect to thescoring func of query UNSEENMAX
-
8/12/2019 Answering Top-k Queries Using Views Updated
35/64
LPTA - Example (cont)
tid11
s11
tid21
tid31
tid41
tid51
s21
s31
s41
s51
tid12
s12
tid2
2
tid3
2
tid42
tid5
2
s22
s32
s42
s52
V1 V2tid1
1
tid1
2
tid21
tid22
Top-1 V1
V2
Qstopping
conditionX1
X2
R(X1, X2)
O (0,0)P (1,0)
R (1,1)
T (0,1)
The algorithm will stop early if the scoring function of the views is
similar to the scoring function of the query.
-
8/12/2019 Answering Top-k Queries Using Views Updated
36/64
LPTA AlgorithmPseudo Code
There is Sequential as well as Random Access.
Sequential access on views
Random Access on base table to find the tuple
http://localhost/var/www/apps/conversion/tmp/scratch_5/LPTA%20algo.dochttp://localhost/var/www/apps/conversion/tmp/scratch_5/LPTA%20algo.doc -
8/12/2019 Answering Top-k Queries Using Views Updated
37/64
-
8/12/2019 Answering Top-k Queries Using Views Updated
38/64
Determining Factor for
performance LPTA versus TA
Highly correlated: every sequential access incurs a random
access.
As a result the determining factor for the performance is
(distance from the beginning of the view each algorithm hasto traverse (read sequentially) before coming into a halt with
the correct answer) X (the number of views participating in
the process).
d=number of lock-step r = no of views
Running Cost:
O(dr)
-
8/12/2019 Answering Top-k Queries Using Views Updated
39/64
Outline
LPTA Algorithm
View Selection Problem
-
8/12/2019 Answering Top-k Queries Using Views Updated
40/64
View Selection Problem
Given a collection of views V = {V1,,Vr} and a
Query Q, determine the most efficient subset U C V
to execute Q on.
Conceptual discussion of View Selection
Two attribute relation (in two dimension)
Multi attribute relation (for any dimension)
Domain of each attribute is normalized to [0,1]
M-attribute relation is refer as m-dimension
View Selection Two Dimension(same side)
-
8/12/2019 Answering Top-k Queries Using Views Updated
41/64
View SelectionTwo Dimension(same side)
Min top-k tupleQ
V1
V2
O (0,0)
T (0,1)
P (1,0)
R (1,1)
X
Y
Square
OPRT
Two views V1 and V2 and Query Q are represented by vectors.
Both the view vectors are to the same side (clockwise) of the query vector
A
B
B1B2
M
AB 1 Q passes through M & intersect unit squareABRTop-k tuples
ABPOTRemaining tuples
Sorted access to V1sweeping line1 to V1 from infinity to origin
Stopping
condition for V1:
sweepline
crosses AB1
bcoz convex
polygon
AB1POT
unseen tuplesand
score(unseen)
-
8/12/2019 Answering Top-k Queries Using Views Updated
42/64
View SelectionTwo Dimension(same side)
Conclusion
V2 is slower compared to V1
If several views in two dimension are available &
all their vectors are to one side of query vector,then it is optimal for LPTA to use the vector that is
closet to the query vector.
-
8/12/2019 Answering Top-k Queries Using Views Updated
43/64
Estimating the Number of Tuples
Estimating and Comparing the Number of Tuples by
simply comparing the areasof respective triangles.
Such approach: Need to have an uniform
distributionwithin the triangles, which is often quite
unrealistic.
In our approach for view selection,
utilize the conceptual conclusions + borrow
knowledge of actual data distribution.
Vi S l ti T
-
8/12/2019 Answering Top-k Queries Using Views Updated
44/64
View SelectionTwo
Dimension(either side of query)
A
B
Min top-k tupleQ
V1
V2
O (0,0)
T (0,1)
P (1,0)
R (1,1)
X
Y
A1
B1
M
Can use only V1 or only V2 for execution
If uses only v1
to answer the
query thestopping
condition will be
reached once the
sweepline
perpendicular tov1 crosses
position A1B/
For V2 - AB1
View Selection Two
-
8/12/2019 Answering Top-k Queries Using Views Updated
45/64
View Selection Two
Dimension(either side of query)
A
B
Min top-k tupleQV1
V2
O (0,0)
T (0,1)
P (1,0)
R (1,1)
X
Y
A1
B1
M
Running LPTA on both V1 and V2,
rather than just running on only one ofV1 or V2? Two views are better than
oneA11
B11
A21
B21
The intersection point of the sweep
lines perpendicular to v1 and v2 ison the line AB
The stopping
condition isreached when the
sweeplines resp
crosses A11B11
and A21B21 such
that
1) intersection pt
of A11B11and
A21B21is on line
AB
2)NumTuples(A11B11R) = NumTuples(A21B21PR) since algo sweeps each view in lock-step
-
8/12/2019 Answering Top-k Queries Using Views Updated
46/64
LPTA on both Views versus One
For two views the position of each sweepline is beforetherespective stopping positions if only one view has been
used.
Total number of sorted accesses for two views:
NumTuples (A11B11R) + NumTuples (A21B21R) = 2
NumTuples (A11B11R)
If Min (NumTuples (A1BR), NumTuples (AB1PR), 2 NumTuples
(A11B11R)) = NumTuples (A1BR) - Use V1 If Min (NumTuples (A1BR), NumTuples (AB1PR), 2 NumTuples
(A11B11R))= NumTuples (AB1PR) - Use V2
Else use both V1 V2
-
8/12/2019 Answering Top-k Queries Using Views Updated
47/64
Theorem for Two Dimensional Case
Theorem 1: Set of Views = {V1,,Vr} Query = Q
Two Dimensional dataset
Va= Closest to query in AnticlockwiseVc= Closest to query in Clockwise
So they are on either side of the query
Optimal execution of LPTA requires the use of eitherVa or Vc i.e., the use of subset from {Va, Vc}
-
8/12/2019 Answering Top-k Queries Using Views Updated
48/64
View SelectionHigher Dimension
Extension of Theorem 1
Theorem 2: Set of Views = {V1,,Vr} Query = Q
m-dimensional datasetOptimal execution of LPTA requires the use of subset
of views U C V such that |U|
-
8/12/2019 Answering Top-k Queries Using Views Updated
49/64
Outline
LPTA Algorithm
View Selection Problem
Cost Estimation Framework
C t E ti ti F k
-
8/12/2019 Answering Top-k Queries Using Views Updated
50/64
Cost Estimation Framework
Running LPTA
Cost Estimation Framework: The cost of running LPTAwhen a specific set of views is used to answer a query.
Cost = total number of sequential accesses in a view
Uses 2 views to answer a query
Cost= 6 sequential
accesses
Min top-k tuple
Can we find that cost
without actually running
LPTA?
A
B
QV1
V2
C t E ti ti F k
-
8/12/2019 Answering Top-k Queries Using Views Updated
51/64
Cost Estimation Framework
without Running LPTA
EstimateCost(Q, U): Returns an estimate of the cost
of running LPTA on exactly this set of views: U
Used within SelectViews(Q,V) to search the subset
U that minimizesEstimateCost(Q,U)
EstimateCost(Q,U) takes into account
Multi-attribute views
Non-uniform data distribution
Si l i LPTA Hi
-
8/12/2019 Answering Top-k Queries Using Views Updated
52/64
Simulating LPTA on Histograms
rather than on views U
Equi-depth histograms:The number of tuples in
each bucket is the same
Base Table R : n tuples (10)
HiEqui-depth histogram
b buckets2buckets : represent the distribution of
points along the Xiattribute
Each bucket will represent n/b data points
10/2 = 5 data points
Si l i LPTA Hi
-
8/12/2019 Answering Top-k Queries Using Views Updated
53/64
Simulating LPTA on Histograms
rather than on views U
In our estimation procedure:
HQrepresents the distribution of score of all tuples
of the database according to the scoring function Q
Cannot calculate the score of all tuples, so
approximate HQ
Si l ti f LPTA Hi t
-
8/12/2019 Answering Top-k Queries Using Views Updated
54/64
Simulation of LPTA on Histograms Simulate LPTA in a
bucket by bucket loc
step to estimate thecost.
HQ HV1 HV2
topkmin
HQ: approximates the score
distribution of the query Q b buckets histograms for
the score distribution of
views
n/b tuples per bucket
Cost
We cannot afford to run LPTA on views U
Pre-estimate topkminbcoz we do not
have access to actual tuples or their
tids. The value of topkminis estimated
from HQby determining the bucket
that contains the kth highest tuple.Since topkminis very likely inside this
bucket we use linear interpolation
with in the bucket to estimate the
topkmin
Cheap procedurebecause we have one iteration of the
LPTA algorithm for every n/b tuples using the valuesfrom the bucket boundaries.
Approx the value of func
-
8/12/2019 Answering Top-k Queries Using Views Updated
55/64
Calculating the Estimated cost
Number of buckets visited along each views = d(3)Number of views = r1(2)
Number of tuples per bucket n/b (10)
Compute the smallest number of tuples n1need to bescanned from the last bucket before stopping
Estimated number of sorted access ((d-1)n/b +n1) r1
((2)(10) + 2) 2 = 44 Therefore running time is
O((d-1) + logn1
) lock-step iteration
-
8/12/2019 Answering Top-k Queries Using Views Updated
56/64
Outline
LPTA Algorithm
View Selection Problem
Cost Estimation Framework
View Selection Algorithms
EstimateCost(Q U) Pseudo
http://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%202%20EstimateCost.dochttp://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%202%20EstimateCost.doc -
8/12/2019 Answering Top-k Queries Using Views Updated
57/64
EstimateCost(Q, U)Pseudo-
code
SelectViews(Q, V): Select the subset of views U
which minimizes the EstimateCost
Exhaustive (E) Approach:Estimate the cost of all
possible subsets of V and select the subset of views
with the smallest cost.
Feasible for database with few attributes
Greedy Approach: Keep expanding the set of views
to use until the estimated cost stops reducing.
SelectViews(Q,V)Pseudo code
http://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%202%20EstimateCost.dochttp://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%203%20SelectV%20iews.dochttp://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%203%20SelectV%20iews.dochttp://localhost/var/www/apps/conversion/tmp/scratch_5/Algorithm%202%20EstimateCost.doc -
8/12/2019 Answering Top-k Queries Using Views Updated
58/64
Requires the solution of a single linear program. Fix the score sUniform Data distribution & very cheap
Maximize the scoring function of the query Max(fq) using theinequalities that scoring function of each view
-
8/12/2019 Answering Top-k Queries Using Views Updated
59/64
-
8/12/2019 Answering Top-k Queries Using Views Updated
60/64
Outline
LPTA Algorithm
View Selection Problem
Cost Estimation Framework
View Selection Algorithms
Experimental Evaluation
-
8/12/2019 Answering Top-k Queries Using Views Updated
61/64
Experimental Evaluation
Two types of dataset: Real and synthetic (uniformand zipf data with varying skew distribution)
The real dataset contains 30K tuples from a website
specialized on automobiles. Experiments Conducted:
Performance comparison of LPTA, PREFER and
TAPerformance of LPTA using each of the view
selection algorithms
Scalability of the LPTA algorithm
Performance comparison of
-
8/12/2019 Answering Top-k Queries Using Views Updated
62/64
Performance comparison of
LPTA, PREFER and TA
Uniform dataset, 3dReal dataset, 2d
-
8/12/2019 Answering Top-k Queries Using Views Updated
63/64
Conclusions
Using views for top-k query answering
LPTA: linear programming adaptation of TA
View selection problem, cost estimation framework,view selection algorithms
Experimental evaluation
-
8/12/2019 Answering Top-k Queries Using Views Updated
64/64
References
Answering Top-k Queries Using Views:
Gautam Das, Dimitrios Gunopulos, Nick Koudas
Optimal Aggregation Algorithms for Middleware :
Ronald Fagin, Amnon Lotem & Moni Naor
aitrc.kaist.ac.kr/~vldb06/slides/R13-1.ppt