Discovering Functional Dependencies and Association Rules by - PowerPoint PPT Presentation

1 Discovering Functional Dependencies and Association Rules by Navigating in a Lattice of OLAP Views Team LIS – IRISA (Rennes, France) Pierre Allard, pierre.allard@irisa.fr Sébastien Ferré, ferre@irisa.fr Olivier Ridoux, ridoux@irisa.fr

2 Introduction : photos database Name Date Place Theme … • Rules extraction from multi- valued set T1 ▫ Set of described photos T2 … Date  Place • Traditional systems extract a Date, Camera  Place list of rules (Date  Camera, Tp) Place = Marseille  Event = Conference … • Many extracted rules • Difficulty in finding relevant information

3 Problem: Rules browsing • Excessive number of rules ▫ Difficulty in finding relevant information • Do not present all the rules together ▫ Show rules in subsets (views) • Need interactivity ▫ Navigation ▫ Exploration

4 Rule extraction : background • Multi-valued context ▫ Attr(r) = {Name, Date, Place, Theme , Event, Camera, …} ▫ Set of items described in each attribute • Functional Dependencies ▫ Date  Place • Conditional Functional Dependencies ▫ (Date  Camera, {Topic = Holiday}) • Association Rules ▫ (Place = Marseille)  (Event = Conference JIGOT) • Hierarchy between rules with help of FCA [Medina09] ▫ Most general to most specific ▫ AR < CFD < FD

5 OLAP : background • On-Line Analytical Processing (OLAP) [Codd93] • Business intelligence (statisticians) • Extract trends, charts from a relation • Cube: view of the relation ▫ Fixed dimensions and measure ▫ Aggregation ▫ Granularity levels ▫ Navigation from cube to cube

6 Projection of a relation into an OLAP cube • Cube = aggregations of measures, by each value of each dimension • Dimensions  measure ▫ Dim(c) = ( Orientation , Date ), Meas(c) = Camera • Premises  conclusion ▫ Orientation , Date  Camera ? • Classic cube with no aggregation ▫ Multisets at each cell ▫ Keep all the values of the measure  Support, confidence • Subset of rules, regarding dimensions and measure

7 View : Photo database Projection (Orient, Date) Landscape Portrait Dim(c) = (Orientation, Date) 16 jul {{Nikon, Nikon}} {{Nikon}} Meas(c) = Camera 21 aug {{Nikon, Apple}} Name Date Orientation Camera t_1 DSC01 16 jul Landscape Nikon t_2 DSC02 16 jul Landscape Nikon t_3 DSC03 16 jul Portrait Nikon t_4 DSC04 21 aug Portrait Nikon t_5 IMG05 21 aug Portrait Apple

8 View : Rules in projection (Orient, Date) Landscape Portrait 16 jul {{Nikon, Nikon}} {{Nikon}} 21 aug {{Nikon, Apple}} • Cell with a unique item value ▫ Association Rule (Orientation = Landscape), (Date = 16 jul)  (Camera = Nikon) • Subset of cube with empty or unique item values cells ▫ Conditional Functional Dependency (Orientation, Date  Camera, {Orientation = Landscape}) • Cube with empty or unique item values cells ▫ Functional Dependency

9 View : Rules in projection • Respected hierarchy regarding number of cells ▫ AR < CFD < FD • Display of all rules type in one view • Accessible support et confidence • Must navigation to access other cubes

10 View : Granularity • Dimension values : taxonomy organized in levels • New possibilities of cube creation, regarding new dimensions and measures ▫ e.g. date month , date year , place country date year 2010 date month 2010/01 2010/02 date day 2010/01/09 2010/01/16 2010/02/06 2010/02/07

11 View : Granularity 16 jul 09 17 jul 09 28 jan 10 • Date  Place {{Marseille}} {{Marseille}} {{Hammamet}} jul 09 jan 10 • Date month  Place {{Marseille, Marseille}} {{Hammamet}} • Add new relevant information • Need navigation links, to go from a cube to another

12 Navigation • Use of standard OLAP navigation links ▫ Add / Delete a dimensions ▫ Drill-down / Roll-up a dimension  Delete is a specific case of roll-up • Introduction of new navigation links ▫ Drill-down / Roll-up the measure ▫ Change measure

13 Navigation : predictable consequences • Rules appearance  Unic item value   Multiple item values • Rules disappearance  Cube with FD  Cube without FD • Rules preservation

14 Abilis : Prototype • Based on Logical Information System (LIS) ▫ Camelis kernel with web interface [Ferre09] ▫ Query and navigate context with logical formulas • Current query ▫ Allows you to select a subset of items (conjonctions, disjonctions, negations, etc) • Navigation tree ▫ Summary of the dimensions values according to the current selection • OLAP navigation links ▫ Granularity created by LIS

15 Initial view : query, navigation tree and extension

16 Navigation tree = index

17 Add « Theme » partition

18 Create complex selections

19 Add « event » as measure

20 Set « date » by day as partition : FD

Roll-up date by day to date by month : CFD 21

22 Add « Camera model » as partition : CFD

23 Remove birthday events : FD

24 Conclusion and perspectives • Show FD, CFD and AR in one view • Predict some consequences on rules with navigation links • Manage more complex data (relational, with zero or multiple attributes) • Add indicator values for navigation links • Allow user to create cubes from aggregated data

25 Thank your for your attention Team LIS – IRISA (Rennes, France) Pierre Allard, pierre.allard@irisa.fr Sébastien Ferré, ferre@irisa.fr Olivier Ridoux, ridoux@irisa.fr