Prefetching for Visual Data Exploration

Punit R. Doshi, Elke A. Rundensteiner, Matthew O. Ward

Computer Science Department

Worcester Polytechnic Institute

Support: NSF grants IIS-9732897, EIA-9729878, and IIS-0119276.

2

Overview

• Why visually explore data?

– Fact: Increasing data set sizes

– Need: Efficient techniques for exploring the data

– Possible solution: Interactive Data Visualization -- humans can detect certain patterns better and faster than data mining tools

• Why cache and prefetch?

– Interactive data visualization tools do not scale well

– Interactive real-time response needed

– Caching and prefetching improve response time.

• Goal: Propose and evaluate prefetching for visualization tools

3Data Hierarchy

Flat Display

Hierarchical Display

Example Visual Exploration Tool: XmdvTool

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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:

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Characteristics of a Visualization Environment

Characteristics that can be exploited for caching and prefetching:

• Locality of exploration• Contiguity of user

movements• Idle time due to user

viewing displayMove left/right

Move up/down

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• Purpose• reduce response time and network traffic

• Issues• visual query cannot directly translate into object IDs high-level cache specification to avoid complete scans

• Semantic Caching: queries are cached rather than objects• minimize cost of cache lookup• dynamically adapt cached queries to patterns of queries

Overview of Semantic Caching

DBcache

Server machineClient machine

GUI

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In XmdvTool, caching reduced response time by 85%

Effectiveness of Caching

0

40

80

120

160

200

Client OFFServer OFF

Client OFFServer ON

Client ON ServerOFF

Client ON ServerON

Caching

Res

po

nse

Tim

e (s

eco

nd

s)

Prefetching can further improve response time.

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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

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m(n-2)

m(n-1)m(n)

m(n+1)

Exponential Weight Average Strategy

m(n-2)

m(n-1)m(n)

m(n+1)

Mean Strategy

Vector Strategies

Hot Regions

Current Navigation

Window

Focus Strategy

Data Set Driven Strategy

(m-1) m (m+1)

Direction Strategy

Localized Speculative Strategies

Random Strategy

1/41/4

1/4

1/4

Prefetching Strategies

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Used:– C/C++

– TCL/TK

– OpenGL

– Oracle 8i

– Pro*C

User

MinMaxLabeling

SchemaInfo

Hierarchical Data

RewriterTranslator

Loader

BufferQueries

GUI

OFF-LINE PROCESS

Estimator

ExplorationVariables

DB

ON-LINE PROCESS

CACHE

Flat Data

PrefetcherLibrary:RandomDirection

Focus

EWAMean

DB DB

Buffer

XmdvTool Implementation

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Evaluation of Prefetching Strategies• Setup:

– Testbed: XmdvTool freeware system for n-dimensional exploration

– User Traces:• Synthetic user traces with varying # of hot regions,

% directionality, average delay between user requests• Real user traces collected by a user study

• Study effect of different navigation patterns:– # hot regions– erratic vs. directional– delay between user requests

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Focus strategy best as # hot regions increases

Prefetchingimproves response time

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5

Number of Hot Regions

Norm

aliz

ed L

aten

cyNo Prefetch

Random

Direction

Focus

Mean

EWA

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Random Strategy – best for erratic traces.Direction Strategy – best for directional traces.

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

'Keep Direction' factor

No

rmal

ized

Lat

ency

No Prefetch

Random

Direction

Focus

Mean

EWA

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Prefetcher performance improves and plateaus as delay between user operations increases.

Prefetcher performance improved up to 28%.

Recall: Caching improved response time by 85% over no caching.

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8

Delay between User Operations (seconds)

Per

cent

age

Impr

ovem

ent (

%)

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What Can We Conclude?• Focus: hot region calculation overhead• Mean and EWA: offers more than needed• Direction: simple, no prior knowledge required

NOTE:• Our experiments on real user traces show that real

users are highly directional

If only one strategy can be chosen, select Directional Prefetching.

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Related Work

• Integrated visualization-database systems -- Tioga, IDEA, DEVise

[have not used caching and prefetching]

• Prefetching research -- mostly on (1) web prefetching, (2) prefetching for memory caches by OS, (3) I/O prefetching.

[no prefetching research for visualization apps]

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Contributions

• Identified key characteristics of visualization tools exploitable for optimizing data access performance

• Developed, implemented and tested prefetching strategies in XmdvTool

• Shown that caching coupled with prefetching at client-side improves data access performance– Caching reduces response time by 85% over no-caching.

– Prefetching further improves response time by 28% over no-prefetching.

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Future Work

No single prefetcher works best for all types of user navigation patterns

Adaptive Prefetching (preliminary results show that this further improves response time and reduces prediction errors, at a minimal overhead cost).

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Thank You

XmdvTool Homepage:

http://davis.wpi.edu/~xmdv

xmdv@cs.wpi.edu

Code is free for research and education.

Contact author: rundenst@cs.wpi.edu