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 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

3 Data Hierarchy Flat Display Hierarchical Display Example Visual Exploration Tool: XmdvTool

4 Structure-Based Brush2Parallel Coordinates (Linked with Brush2) Roll-Up: Structure-Based Brush1Parallel 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

6 Overview of Prefetching Locality of exploration Contiguity of user movements Idle time due to user viewing display New user query Idle time Prefetchin g Cache DB User’s next request can be predicted with high accuracy Time to prefetch Fetchin g

7 (m-1)m(m+1) Direction Strategy Random Strategy 1/4 Static Prefetching Strategies

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.

9 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

10 Strategy Selection Requirements for strategy selection: 1.Set of strategies to select from 2.Performance measures 3.Fitness function 4.Strategy selection policy

11 Set of Strategies & Performance Measures Strategy #Correctly Predicted #Not Predicted #Mis- Predicted No Prefetch Random Direction Performance measures Strategies YesNo Yes Correctly predicted Mis-predicted No Not predicted Required by user Predicted by prefetcher

12 Fitness Function Strategy#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 Cost of No prediction Cost of Mis-prediction

13 Fitness Function Definitions Global Average: Local Average (using exponential smoothing):

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 Random Direction Overall

15 Performance Evaluation Setup – 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

16 Directional User: Navigation Patterns Over Time Ave 73% directional Ave 70 queries/min Navigation pattern changes over time

17 Directional User: Navigation Patterns Over Time Move up or down then move left to right to left

18 Directional User: Directional prefetcher is best Selection matched more directional navigation pattern. Any kind of prefetching is better than none.

19 … but SelectBest is even better SelectBest chose Directional & No-Prefetching No-Prefetching selected when #queries/min is high & %dir is low.

20 Directional User: Other performance measures Misclassification cost = trade-off between %NP & %MP. SelectBest gave low %NP and high %MP.

21 Directional User: Other performance measures SelectBest gave best %CP & response time but this will not always be the case. Choice of fitness function is important.

22 Ave 50% directional Ave 40 queries/min Pattern changes over time Move left then perturb up & down. Move right then perturb up & down. Indeterminate User: Navigation Patterns Over Time

23 Indeterminate User: SelectBest is better SelectBest chose Random & No-Prefetching No-Prefetching selected when #queries/min is high & %dir is low.

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.

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: Code is free for research and education. Contact author: