Verklaren van exceptionele waarden in multi-dimensionele bedrijfsdatabanken

Emiel Caron, November 14, 2013

Business Intelligence

• Stichting Aanpak voertuigcriminaliteit (AVc)

• Goal AVc: reduction of vehicle crime by means of prevention and by supporting public partners

• Important way to support the tackling of vehicle crime is to perform analyses on vehicle criminality data

National Information Centre Vehicle Crime

Multi-dimensional model

Vehicle crime facts

gestolen_voertuigen(sum)teruggevonden_voertuigen(sum)niet_teruggevonden_voertuigen(sum)teruggevonden_ratio(afterroll-up)cataloguswaarde_voertuigen(avg)dagwaarde_voertuigen(avg)leeftijd_voertuig_moment_diefstal(avg)aantal_dagen_vermist(avg)

Datum voertuig gestolen

Jaar (10)Kwartaal (40)Maand (120)

Datum voertuig teruggevonden

Jaar (10)Kwartaal (40)Maand (120)

Postcode gebied voertuig gevonden

gevonden_nederland/buitenland (2)Post_code_pos_1Post_code_pos_2Post_code_pos_3Post_code_pos_4Post_code_pos_5Post_code_pos_6

Postcode gebied voertuig gestolen

Gestolen_nederland/buitenland (2)Post_code_pos_1Post_code_pos_2Post_code_pos_3Post_code_pos_4Post_code_pos_5Post_code_pos_6

Voertuigclassificatie

Voertuig_categorie (5)Voertuig_merk (4015)Voertuig_type

Measures

Dimensions

Dimension hierarchy

Vehicle crime data cube

Time

Vehic

le

Lo

cati

on

Sum

Sum V1

V3V2

Q1 Q2 Q3 Q4

A

B

C

Sum

All, All, All

Annual number of stolen vehicles in Location “A” for Vehicle “V1”

Navigational operators: Roll-up, Drill-down, Slice, Dice…

“How can multi-dimensional databases be extended with explanatory analysis?”

• Value which is significantly different from expected value based on a normative model

• Normative models:− Managerial models− Statistical models

Chapter 3: Exceptions in mult-dimensional data

actual referencey y y

• Events are explained by giving their causes

Chapter 4: General explanation formalism

3-place relation: 1 actual object a profit(2012.Q1, Spain, All-Products)

2 reference object r profit(2011.Q1, Spain, All-Products)

3 property F profit in 2012 low compared to 2011

because Event , ,

despite

contributing causesa F r

counteracing causes

• Explanations are based on equations

1 2( , , , )ny f x x x

• Business model equations

Systems of equations in OLAP

Revenues

Profit

Cost of Goods

Volume

Volume

Variable Cost Indirect CostUnit Price

Unit Cost Variable Cost

T[Year]

T[Quarter]

T[Month]

..... .....

..... .....

2002 2012

Q1-02 Q4-02 Q1-12 Q4-12

Jan Feb Mrt Jan FebMrtNov DecOct NovOct Dec

.....

AllT[All]

• Drill-down equations

Case study: Sales analysis (exception identification)

2311 2ˆ (Country,Pers.Acces.) (Country) (Pers.Acces.)y

Low exception: c = (2001, U.S.A., Binoculars)

∂revenues231(2001, U.S.A, Binoculars)="low"

Quarter

(Oct, ., .)(Nov, ., .)

(Q1, ., .) (Q2, ., .) (Q3, ., .) (Q4, ., .)

0.290.270.260.18

(Dec, ., .)

(Jul, ., .)(Aug, ., .)

(Sep, ., .)

(Apr, ., .)(May, ., .)

(Jun, ., .)

(Jan, ., .)(Feb, ., .)

(Mrt, ., .)Month

0.440.31

0.26

0.350.29

0.37

0.310.31

0.37

0.420.26

0.31

0.24

0.34

0.30

0.12

(., Boston, .)

(., LA, .)

(., Seattle, .)

(., Miami, .)

City

0.35

0.30

0.08

0.22

(., ., Seeker 35)

(., ., Seeker 50)

(., ., Seeker Extreme)

(., ., Seeker Mini)

Product

Explanation trees that partially explain the exceptional cell cin the Product, Time & Location dimension

Case study: Sales analysis (explanation)

Business applications

• Sales & financial analysis

• Variance analysis in accountancy

• Continuous auditing/ Risk assessment

• Competition benchmarking