1. OLAP tool for comparing time- based data 14 th May 2008 Proposed By Pimolmas Ponchaisakuldee 104915 Advisor Dr. Paul Janecek

2. Contents Introduction 1 Implementation 2 Experimental Evaluation 3 Conclusion 4

3. What is Data warehouse & OLAP  Data warehouse  a very large database with special characteristic  Data for analyzing  OLAP tool (On-Line Analytical Processing)  A tool on top of data warehouse  For data exploring and analyzing 3

4. OLAP tool Problems  Cube navigation  Data presentation 4

5. OLAP tool Problems  Cube navigation  Problems of window-explorer-like tree browser Lose in tree topology Need several clicks  Requirements Overall topology Multiple focuses and topology at the same time  Solutions DOITree browser (better topology representation, multiple focuses, and go more than one level of hierarchy per click) DOITree VS Window-Explorer-like browser 5

6. OLAP tool Problems  Data presentation  Tasks Trend finding over time Trend comparison between 2 period of time  Problems Pivot table: hard to do the analysis task Existing graph: complex, hard to focus Polaris table –does not support hierarchy exploring of data –Problem of composition for comparison task  Solution Focus on demand graph = graph matrix + overlaid graph 6

7. Objectives  To build OLAP tool  To evaluate effectiveness of DOITree visualization for OLAP-specific navigation compare to Window-Explorer-like visualization  To evaluate visual tools for time-based comparison task Graph matrix Overlaid graph 7 Focus on demand graph

8. Contents Introduction 1 Implementation 2 Experimental Evaluation 3 Conclusion 4

9. Implementation  Java to modified JRubik 9 Cube Navigator Pivot table presentation Graph matrix presentation Overlaid Graph

10. Design when measure is on column 10

11. Design when measure is on row 11

12. Contents Introduction 1 Implementation 2 Experimental Evaluation 3 Conclusion 4

13. Evaluation 1: (WD) VS (DOI) Window-Explorer Like browser (WD) of JRubik DOI tree browser (DOI) of the modified JRubik 13

14. 14 Evaluation 2: (GE) VS (FOCUS) Overlaid Graph General graph (GE) Focus on demand graph (FOCUS) Graph Matrix + VS

15. Experiment design  Participants will be trained to use the 2 tools  The modified JRubik = (DOI) and (FOCUS)  JRubik = (WD) and (GE)  Experiment design with counterbalancing order 15 ParticipantsFirst ToolSecond ToolData Student01 to Student10 The modified JRubikJRubikFoodmart Student11 to Student20 JRubikThe modified JRubikFoodmart Staff1 and 2The modified JRubikJRubikAIT admission Staff3 and 4JRubikThe modified JRubikAIT admission

16. First Evaluation: (WD) VS (DOI)  Tasks for browser comparison  Only browsers (one attribute)  Appendix C page 83 Task1 - First Node Finding Task2 - First time Node Finding Task3 - Subtree Revisiting Task4 - Node Revisiting  The browsers as query tool (many attributes)  Appendix D page 84 Task1 - First time Node Finding Task2 - Subtree Revisiting Task3 - Node Revisiting 16 WD DOI WD DOI

17. Second Evaluation: (GE) VS (FOCUS)  Tasks for graph comparison  Simple Analysis Trend finding, Trend comparing  Complex Analysis Trend comparing 17

18. Second Experiment: (G) VS (F)  Simple Analysis  Complex Analysis 18

19. Variables studied using Foodmart 19 Browsers comparisonGraphs comparison (WD) VS (DOI)(GE) VS (FOCUS) Independent variables Task types Cube Structure Browser Type Screen area Participant demographics Task types Presentation type Screen area Participant demographics Dependent variables Speed to complete tasks Number of clicked nodes Satisfaction score Speed to complete tasks Number of correct answer Satisfaction score

20. Variables studied using AIT ADM 20 Browsers comparisonGraphs comparison (WD) VS (DOI)(GE) VS (FOCUS) Independent variables Task types Cube Structure Browser Type Screen area Participant demographics Task types Presentation type Screen area Participant demographics Dependent variables Speed to complete tasks Number of clicked nodes Satisfaction score Speed to complete tasks Number of correct answer Satisfaction score

21. Experiment Result  Browser comparison result  Browsers only: Pair-Sample T-test  Browser as a query tool: Pair-Sample T-test  Participants opinions: One way ANOVA  Graph comparison result  Simple and Complex Analysis Pair-Sample T-test One way ANOVA  Participants opinions One way ANOVA 21

22. Experiment Result  Browser comparison result: browsers only  Subtree revisiting – users can leave subtrees open 22 First time node finding First node finding Subtree revisiting Node revisiting First time node finding First node finding Subtree revisiting Node revisiting Sig. DOI Sig. WD WD DOI Sig. WD Sig. DOI DOI WD

23. Experiment Result  Browser comparison result: browsers only  Finding for DOITree Advantage –More information help users to recover from mistake Disadvantage –More information can distract users to go wrong path 23

24. Experiment Result  Browser comparison result: browser as a query tool 24 First node finding Subtree revisiting Node revisiting WD DOI Sig. DOI Sig. WD WD DOI

25. Experiment Result  Browser comparison result: browser as a query tool  (DOI) always take less clicks  Go more than one level per click using (DOI)  Users shrink subtree before finding new node using (WE) 25 First node finding Subtree revisiting Node revisiting 25 WD DOI Sig. DOI Sig. WD WD DOI

26. Experiment Result  Browser comparison result: Opinions  Likeness: auto shrink, go more than one level  Dislikeness: dizzy (many subtrees open), cannot leave subtree open 26

27. Experiment Result  Graph comparison result 27 FOCUS GE Sig. FOCUS Sig. GE GE FOCUS

28. Experiment Result  Graph comparison result : Opinions  Likeness: focus  Dislikeness: graph and label are too small 28

29. Experiment Result  ANOVA: 3 groups of student participants  Satisfaction score on tree browsers: no sig. was found  Satisfaction score on graphs: no sig. was found  Simple Analysis: Next slide  Complex Analysis: no sig. was found 29

30. Experiment Result  ANOVA: 3 groups of student participants  Simple analysis task 30 G2: OLAP using G1: OLAP using & ex G3: Non OLAP ex G1 Sig.: OLAP using & ex Sig. G3 Sig. Non OLAP ex Sig.

31. Experiment Result  ANOVA: 2 groups of student participants 31 G2: JRubik G1: Mod. JRubik G2: JRubik G1: Mod. JRubik

32. Discussion  Personal Evaluation 32 TasksSubtasksJPivotFreeAnaly sis JRubikModified JRubik T4: Analyzing visualization presentation T4.1: How easy to read text of each graph+++++ T4.2: How easy to read exact value in graph+++++++++ T4.3: How easy to find trend of data++++++++ T4.4: How easy to compare trend of data+++ ++++

33. Contents Introduction 1 Implementation 2 Experimental Evaluation 3 Conclusion 4

34.  Problems  Cube navigation, Data Presentation  Objectives  to evaluate effectiveness of (DOI) compare to (WD)  to evaluate the focus on demand graph for time-based data analysis tasks  Browser comparison result: 34 TasksThe betterSig.Note Browsers only First time node finding(DOI)  - First node finding=  - Subtree revisiting(WE) Leave subtree open Node revisiting(DOI)  Some users shrink subtree Browsers as query tool First node finding(DOI) At middle depth (very deep has animation) Subtree revisiting(DOI) When subtree expanded Node revisiting(DOI) When subtree expanded WD DOI

35. Conclusion  Graph comparison result:  Implications  Browser comparison: Browser only: (DOI) is better for long term usage, (WE) is good for revisiting tasks Query tool: DOITree is better for unknown data and suitable for the analyzers who are always analyze unknown data  Graph comparison: separated graph in graph matrix can eliminate complexity Users can focus what they want line graph is a suitable presentation when time is on X axis 35 CriteriaThe betterSig.Note Pair-sample T-Test Simple Analysis Tasks(FOCUS) - Complex Analysis Tasks(FOCUS) - FOCUS GE