1. 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. 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

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

4. 4 Structure-Based Brush2Parallel Coordinates (Linked with Brush2) Roll-Up: Structure-Based Brush1Parallel Coordinates (Linked with Brush1) Drill Down:

5. 5 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 display Move left/right Move up/down

6. 6 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 DB cache Server machineClient machine GUI

7. 7 In XmdvTool, caching reduced response time by 85% Prefetching can further improve response time.

8. 8 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

9. 9 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 1/4 Prefetching Strategies

10. 10 Used: –C/C++ –TCL/TK –OpenGL –Oracle 8i –Pro*C User MinMax Labeling Schema Info Hierarchical Data Rewriter Translator Loader Buffer Queries GUI OFF-LINE PROCESS Estimator Exploration Variables DB ON-LINE PROCESS CACHE Flat Data Prefetcher Library: Random Direction Focus EWA Mean DB Buffer XmdvTool Implementation

11. 11 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

12. 12 Focus strategy best as # hot regions increases Prefetching improves response time

13. 13 Random Strategy – best for erratic traces. Direction Strategy – best for directional traces.

14. 14 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.

15. 15 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.

16. 16 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]

17. 17 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.

18. 18 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).

19. 19 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