A Strategy Selection Framework for Adaptive Prefetching in Visual Exploration Punit R. Doshi,...
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A Strategy Selection Framework for Adaptive Prefetching
in Visual Exploration
Punit R. Doshi, Geraldine E. Rosario, Elke A. Rundensteiner,
and Matthew O. Ward
Computer Science Department
Worcester Polytechnic Institute
Supported by NSF grant IIS-0119276.
Presented at SSDBM2003, July 10, 2003.
2
Motivation
• Why visually explore data?– Ever increasing data set sizes make data
exploration infeasible– Possible solution: Interactive Data Visualization --
humans can detect certain patterns better and faster than data mining tools
• Why cache and prefetch?– Interactive visualization tools do not scale well,
yet we need real-time response
4
Example Visual Exploration Tool: XmdvTool
Structure-Based Brush2 Parallel Coordinates (Linked with Brush2)
Roll-Up:
Structure-Based Brush1 Parallel Coordinates (Linked with Brush1)
Drill Down:
5
Characteristics of a Visualization Environment Exploited for Prefetching
• Locality of exploration
• Contiguity of user movements
• Idle time due to user viewing display
Move left/right
Move up/down
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Overview of Prefetching• Locality of exploration• Contiguity of user
movements• Idle time due to user
viewing display
New user query
Idle time
Prefetching
Cache DB
User’s next request can be predicted with high accuracy
Time to prefetch
Fetching
8
Drawbacks of Static Prefetching
• Lacks a feedback mechanism
• Different users have different exploration patterns
• A user’s pattern may be changing within same session
Generates predictions independent of past performance.
No single strategy will work best for all users.
A single strategy may not be sufficient within one user session.
This calls for Adaptive Prefetching – changing prediction behavior in response to changing data access patterns.
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Types of Adaptive Prefetching
• Fine tuning one strategy:– Change parameter values of one strategy over time
depending on past performance
• Strategy selection among several strategies:– Given a set of strategies, allow the choice of
strategy to change over time within same session, depending on past performance
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Strategy Selection
Requirements for strategy selection:1. Set of strategies to select from2. Performance measures3. Fitness function4. Strategy selection policy
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Set of Strategies & Performance Measures
Strategy#Correctly
Predicted
#Not
Predicted
#Mis-
Predicted
No Prefetch
Random
Direction
Performance measuresStrategies
Yes No
Yes Correctly predicted
Mis-predicted
No Not predicted
Required by user
Predictedby prefetcher
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Fitness FunctionStrategy #Correctly
Predicted
#Not
Predicted
#Mis-
Predicted
Local Avg. Mis-Classification Cost
No Prefetch
Random
Direction
Other fitness functions:
• global average misclass. cost
• local average response time
• global average response time
Fitness function
MPNPCP
MPMPNPNP CCstmisclassco
###
##
Cost of No prediction
Cost of Mis-predictionNPC
MPC
1 MPNP CC
13
Fitness Function DefinitionsGlobal Average:
t
istmisClassCotglobalAvg
t
i 1
Local Average (using exponential smoothing):
11 tlocalAvgtstmisClassCotlocalAvg
14
Strategy Selection Policy
Strategy selection policies:
1. Best
2. Proportionate
Strategy #Correctly
Predicted
#Not
Predicted
#Mis-
Predicted
Local Avg. Mis-Classification Cost
No Prefetch 12 38 86 0.5
Random 10 116 148 0.4
Direction 4 125 107 0.3
Overall 26 279 341 0.4
15
Performance EvaluationSetup –• XmdvTool as testbed• 14 real user traces analyzed• User traces were analyzed for:
• Tendency to move in the same direction• Frequency of movement• Size of sample focused on
• 3 user types: random-starers, indeterminates, directional-movers
We will show:• Detailed analysis and results for 2 user traces• Summary results for all user types
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Directional User: Navigation Patterns Over Time
% directional vs Time
0
10
20
30
40
50
60
70
80
90
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
% d
irec
tio
nal
# queries vs Time
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
# que
ries
•Ave 73%directional
•Ave 70queries/min
•Navigationpattern changes over time
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Directional User: Navigation Patterns Over Time
Move upor downthen moveleft to right to left
Brush movements over time
0
0.5
1
1.5
2
0 1 2 3 3 4 5 5 6 6 7 7 8 9 10 10 11 12 12 13 14 15 17 18 19 19 22 23 24 25 25 26 26 27 28 29
time (mins)
exte
nts
xycenter level
regions visited per 1 min snapshot
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
1 minute snapshots
leve
l
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Directional User: Directional prefetcher is best
cum misclassification cost vs Time
0.4
0.42
0.44
0.46
0.48
0.5
0.52
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
DIR RANDOM NO PREFETCH
Selectionmatchedmore directionalnavigationpattern.
Any kind of prefetchingis better than none.
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… but SelectBest is even better
SelectBestchose Directional& No-Prefetching
No-Prefetchingselected when #queries/minis high & %diris low.
cum misclassification cost vs Time
0.4
0.42
0.44
0.46
0.48
0.5
0.52
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
DIR RANDOM NO PREFETCH BEST
% times selected vs time
0
20
40
60
80
100
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
% ti
mes
sel
ecte
d
% times no-prefetch selected % times random selected % times directional selected
20
Directional User: Other performance measures
Misclassificationcost = trade-offbetween %NP& %MP.
SelectBestgave low%NP andhigh %MP.
%Not Predicted vs Time
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
%N
P
DIR RANDOM NO PREFETCH BEST
%Mis-Predicted vs Time
0
10
20
30
40
50
60
70
80
90
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
%M
P
DIR RANDOM NO PREFETCH BEST
21
Directional User: Other performance measures
SelectBestgave best %CP & response time but this will not alwaysbe the case.
Choice of fitness functionis important.
%Correctly Predicted vs Time
024
68
101214
161820
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
%CP
DIR RANDOM NO PREFETCH BEST
Total Response Time vs Time
0
20000
40000
60000
80000
100000
120000
140000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
time
Tota
l Res
pons
e Ti
me
DIR RANDOM NO PREFETCH BEST
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•Ave 50%directional
•Ave 40queries/min
•Pattern changes over time
•Move leftthen perturbup & down.
Move rightthen perturbup & down.
regions visited per 1 min snapshot
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
1 minute snapshots
leve
l
Brush movements over time
0
0.5
1
1.5
2
0 1 1 2 2 3 3 4 5 5 6 7 8 9 10 11 12 12 13 13 14 15 15 16 16 17 17 18 19 19 20 21 21 22 22 23
time (mins)
exte
nts
xycenter level
Indeterminate User: Navigation Patterns Over Time
23
Indeterminate User: SelectBest is better
SelectBestchose Random& No-Prefetching
No-Prefetchingselected when #queries/minis high & %diris low.
cum misclass cost vs Time
0.35
0.37
0.39
0.41
0.43
0.45
0.47
0.49
0.51
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
time
DIR RANDOM NO PREFETCH BEST
% times selected vs time
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
time
% ti
mes
sel
ecte
d
% times no-prefetch selected % times random selected % times directional selected
24
Summary Across All User Types
Experiments repeated 3x and averaged.
Reduced prediction error for random-starters and directional-movers.
No improvement in response time.
0
10
20
30
40
50
60
70
80
90
100
No
Pre
fetc
h
Random
Direction
Best
No
Pre
fetc
h
Random
Direction
Best
No
Pre
fetc
h
Random
Direction
Best
Random-Starers Indeterminates Directional-Movers
Cluster
No
rm
alized
Resp
on
se T
ime
(A
verag
ed
)0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
No
Pre
fetc
h
Ra
nd
om
Dir
ectio
n
Be
st
No
Pre
fetc
h
Ra
nd
om
Dir
ectio
n
Be
st
No
Pre
fetc
h
Ra
nd
om
Dir
ectio
n
Be
st
Random-Starers Indeterminates Directional-Movers
Cluster
Glo
bal A
verag
e M
iscla
ssif
icati
on
Co
st
(Averag
ed
)
25
Related Work• Adaptive Prefetching –
• Strategy Refinement - Davidson98, Tcheun97, Curewitz93, Kroeger96, Palpanas99
• Learning - Agrawal95, Swaminathan00
• Adaptation Concepts – Mitchell99, Waldspurger94, Avnur00
• Performance Measures – Joseph97,Weiss25, Mitchell99
• Database support for Interactive Applications – Stolte02, Tioga96
26
Observations• Prefetching is better than no prefetching• Different users have different navigation patterns,
same user has varying navigation patterns within same session
• No single prefetcher works best in all cases• Strategy selection allows prefetcher to adapt• Performance of strategy selection depends on
fitness function being optimized
27
Contributions
• The first to study adaptive prefetching in the context of visual data exploration
• A proposed framework for adaptive prefetching via strategy selection, as opposed to common approach of strategy refinement
• Empirical results showing benefits of strategy selection over a wide range of user navigation traces
28
That’s all folks
XmdvTool Homepage:
http://davis.wpi.edu/~xmdv
Code is free for research and education.
Contact author: [email protected]