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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