Visual Comparison of Enterprise Models Using Syntactic Model Information Jean-Paul Van Belle [email protected]

Visual Comparison of Enterprise Models Using Syntactic Model Information

Jean-Paul Van Belle

[email protected]

Visual Comparison of Enterprise Models ACIS'03

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Why research "visualization of models"? You get

interesting results!


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Slides,

waves

androller-coasters


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Research Objective

Find an immediately intuitive characterisation and visualization of the (syntactic & semantic) comparison of models within a particular application domain Notes:

Descriptive – not normative (yet)! Existing metrics not satisfactory Real-world test bed


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Overview

Introduction: The research context

Overview of the Enterprise Models

Problems with SE metrics for models

Graph analysis approach

Fan-out Frequency Distribution

plot & signature

Visual interpretation of semantic distance


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Framework for Analysis & Evaluation of Models

Syntactic Semantic Pragmatic

Absolute Correctness / error-freeIntegrity / ConsistencyConciseness / efficiency Formality / tools support /

executabilityModularity / hierarchy

structuredness /

Genericity (universality & technical independence)

Completeness (coverage of the domain)

Expressiveness

Validity (authority & user acceptance)

Flexibility / expandibility / portability / adaptability

Relative

ComplexityArchitectural style

Perspicuity / Comprehensibility / Understandability / Self descriptiveness

Documentation

Purpose / GoalAppropriateness /

relevancePriceAvailability / Support

See also: Chris Taylor

@ QUT


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The Model Database

Purpose: to serve as a validating test bank for the analysis framework.

Model Selection Criteria: Domain = “The Generic Enterprise” Sufficiently large size

~200 entities; ~300 relationships Publicly available From very different reference disciplines


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The Models: Systems Engineering

Reference Frameworks Purdue Nippon ARRI

OO BOMA Fowler (patterns!) San Francisco

ERD Libraries Silverston Hay


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The Models: Practitioners

ERPs BAAN SAP R/3

Real organisations AKMA NHS

Data warehousing Inmon


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The Models: Various

Ontologies TOVE CYC (subset) AIAI

Miscellaneous Miller (systems

theory) Ottawa (linguistic) Random

Finance Belgian Accounting USB Growth Model


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Non-visual approaches:Model metrics

Structural metrics relate to the number of nodes (entities) and arcs (relationships between the entities).

Inheritance metrics investigate the characteristics of the inheritance trees.

Grouper metrics take into account the modularity of the model by means of the grouper constructs


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Structural metrics

McGabe’s cyclomatic complexity CC = # relationships - # entities + 1

Relative connectivity= # relationships / # entities

DeMarco’s DataBang= REi (# relationships for ith entity) x wi (ith entity’s weighting factor).


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Model ID

# Entitie

s / nodes

/ vertic

es

# Rels = Arcs

= Absol. Connectivity

Cyclomatic

Complexity

Relative

Connectivity

Avge Fan-Out

Max Fan-Out

StdDv of

Fan-out

Data Bang

Avge Data Bang

AIAI 44 73 30 0.78 3.32 12 3.09 220 4.99

AKMA 56 61 6 0.74 2.18 9 1.60 160 2.85

ARRI 119 197 79 1.54 3.31 15 2.85 592 4.97

BAAN 232 608 377 1.85 5.24 43 5.94 2018 8.70

BelgAcc 178 213 36 0.45 2.39 54 4.43 599 3.37

BOMA 128 192 65 1.05 3.00 15 2.58 557 4.35

CYC 639 1149 511 1.48 3.60 105 5.48 3507 5.49

Fowler 95 131 37 1.09 2.76 12 2.37 372 3.92

Hay 235 725 491 2.49 6.17 73 9.12 2470 10.51

Inmon 225 241 17 0.56 2.14 23 2.99 682 3.03

Miller 26 81 56 1.69 6.23 21 5.12 272 10.47


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Structural Metrics: Deficiencies

# Entities/Relationship: a rough size indicator but no proxy for complexity

Cyclomatic Complexity is meaningless (not normalized for model size!)

Relative connectivity & fanout are skewed by outliers

Data bang metrics are of little or no interpretative value


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Visualizing Model Structure

Graph Analysis Packages

Plot of Fan-out Distributions

Descriptive Statistics

Proposed Fan-out Distribution Statistics


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Graph Analysis Packages

The model as a directed graph or network

Many tools available E.g. PAJEK:

Package for Large network Analysis (Vlado Group, Univ Ljubljani, Slovenia)

Problem: visualization “bunch of lines”

The key element = fan-outs!


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BAAN Hay

Inmon Silverst.


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Clustering (Fruchterman-Reingold)


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"bunch of lines"Intuitive? Interpretation?

Essential element of complexity / syntactic structure = network

Essential element of network => connectivity or fan-out

Average fan-out => too simplistic

=> Look at entire distribution of (entity) fan-outs


20Plotting the Fan-out

Frequency Distribution


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Descriptive Statistical Measures

Central location (“average”) Arithmetic; median; mode; harmonic

Dispersion (“spread”) Standard deviation; range

Skewness (“ symmetry”) 1st & 2nd Pearson coeff.; 3rd moment

Peakness / curtosis (“pointedness”) Coeff. of curtosis; adjusted 4th moment


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Descriptive Statistic

N Mean Mo Med Harm StDv Rang Pear1 Pear2 3rd Mt Curtos 4th Mt

Purdue 89 5.03 3 5 3.82 2.68 15 0.04 0.76 1.24 0.29 1.98

Miller 26 6.23 3 4.5 3.28 5.12 21 1.01 0.63 1.39 0.33 1.17

Random 238 3.82 3 4 2.80 1.91 10 -0.28 0.43 0.63 0.38 -0.04

Semi-Random 238 3.82 3 4 2.80 1.91 10 -0.28 0.43 0.63 0.38 -0.04

Hay 235 6.17 2 3 2.42 9.12 73 1.04 0.46 3.93 0.22 18.88

TOVE 539 4.51 2 3 2.33 7.81 127 0.58 0.32 10.24 0.21 138.21

SAP 328 3.73 2 3 2.30 4.89 73 0.45 0.35 9.41 0.17 122.35

Ottowa-Dense 248 3.67 2 3 2.45 3.04 22 0.66 0.55 2.79 0.20 9.85

BAAN 232 5.24 1 3 2.23 5.94 43 1.13 0.71 2.81 0.33 10.52

USB 119 4.02 1 3 2.29 2.80 12 1.09 1.08 0.85 0.29 -0.05

Silverston 209 3.08 1.5 2 1.76 3.60 26 0.90 0.44 3.77 0.17 17.48

CYC 639 3.60 1 2 1.94 5.48 105 0.87 0.47 11.50 0.25 190.72

SanFran 99 3.47 1 2 1.95 2.84 12 1.56 0.87 1.28 0.29 0.84

AIAI 44 3.32 1 2 1.81 3.09 12 1.28 0.75 1.64 0.19 1.66

ARRI 119 3.31 1 2 1.81 2.85 15 1.38 0.81 1.58 0.33 2.63

BOMA 128 3.00 1 2 1.81 2.58 15 1.16 0.78 2.03 0.30 4.43


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Proposed Measures

Curve shape family Waves slides (& rollercoasters)

Bumpiness / smoothness Number of inflexion points Exceeding a minimum threshold (2.5%)

Degree of curvature (“bentness”) Fit the cumulative freq distribution with

y=tanh(x/h) function (1 param, good fit)


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Slides, waves & roller-coasters


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Plotting the Fan-out Frequency Distribution


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Proposed Measures

Curve shape family Waves slides (& rollercoasters)

Bumpiness / smoothness Number of inflexion points Exceeding a minimum threshold (2.5%)

Degree of curvature (“bentness”) Fit the cumulative freq distribution with

y=tanh(x/h) function (1 param, good fit)


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Harmonic Mean

Bumpi-ness (0%)

Bumpiness (2.4%)

Slope of line fit

h coeffTanh(x/h)

SSD

Purdue 3.82 5 5 -0.6% 0.67 6%

Miller 3.28 5 5 -1.5% 0.76 5%

Random 2.80 3 2 -2.0% 0.49 4%

Semi-Random 2.80 3 2 -2.0% 0.49 4%

Hay 2.42 4 3 -2.4% 0.58 2%

TOVE 2.33 1 0 -2.8% 0.46 0%

SAP 2.30 2 1 -3.1% 0.40 1%

Ottowa-Dense 2.45 4 3 -3.0% 0.42 2%

BAAN 2.23 4 3 -2.5% 0.58 3%

USB 2.29 4 4 -2.2% 0.52 0%

Silverston 1.76 5 1 -4.0% 0.30 2%

CYC 1.94 4 1 -3.5% 0.38 1%


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Syntactic Semantic analysis

Syntactic => appearance or shape of model network ("nodes & links")

Semantic => meaning of network => partly captured in e.g. entity names=> "semantic overlap" (or distance) between models


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Semantic Overlap


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Semantic DistanceDendogram


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Conclusions & Future Research

Traditional measures not very useful

Suggested measures seem to work very

well for visualization

Normative interpretation?

Other domains?


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Documents

Visual Comparison of Enterprise Models Using Syntactic Model Information Jean-Paul Van Belle [email protected]