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Semantic Normalization and Matching of Business Dependency Models 18 - - PowerPoint PPT Presentation

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INSTITUTE OF INFORMATION SYSTEMS Semantic Normalization and Matching of Business Dependency Models 18 th IEEE Conference on Business Informatics Alexander Motzek Ralf Mller Universitt zu Lbeck Institute of Information Systems

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INSTITUTE OF INFORMATION SYSTEMS

Semantic Normalization and Matching of Business Dependency Models

18th IEEE Conference on Business Informatics

Alexander Motzek∗ Ralf Möller∗

∗Universität zu Lübeck

Institute of Information Systems Ratzeburger Allee 160, 23562 Lübeck, Germany {motzek,moeller}@ifis.uni-luebeck.de

August, 29th 2015

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Motivation

▸ companies becomes more and more connected. ▸ companies depend on their infrastructure. ▸ ultimate goal: assure company’s success

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Motivation

▸ companies becomes more and more connected. ▸ companies depend on their infrastructure. ▸ ultimate goal: assure company’s success - but how?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Infrastructure = Company?

▸ complex infrastructure dependencies. ▸ this is a real world model from an exercise. ▸ theorem: this is the company.

→ infrastructure success==company success.

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company

▸ something fails or is attacked.

→ local impact.

▸ global impact on company?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company

▸ something fails or is attacked.

→ local impact.

▸ global impact on company? ▸ might even spread...

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company

▸ something fails or is attacked.

→ local impact.

▸ global impact on company? ▸ might even spread... ▸ ...to dependent nodes...

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company

▸ something fails or is attacked.

→ local impact.

▸ global impact on company? ▸ might even spread... ▸ ...to dependent nodes... ▸ ...to dependent nodes...

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company

▸ something fails or is attacked.

→ local impact.

▸ global impact on company? ▸ might even spread... ▸ ...to dependent nodes... ▸ ...to dependent nodes... ▸ until everything is impacted.

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company?

▸ yes, there is a global impact on the company! ▸ should one defend? ▸ remove source of evil? ▸ may be worse...

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Assuring success of a company?

▸ yes, there is a global impact on the company! ▸ should one defend? ▸ remove source of evil? ▸ may be worse... ▸ coffeemachine or nuclear control server?

coffeemachine-factory or power plant?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Infrastructure ≠ Company

▸ a company is more than an infrastructure. ▸ some are better than others. ▸ business critical resources.

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Model

BF1 BF2 BF3 BF4 BP1 BP2 CM1 A B C D

▸ business critical devices provide

services, i.e., business functions, supporting accomplishment of business processes.

▸ two perspectives on company ▸ goal: understand the company

understand the infrastructure protect the company

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Understanding the Company

▸ how to acquire this knowledge? ▸ someone who understands the big fussy ball does not know this. ▸ someone who understands business processes does not understand the big ball. ▸ different expertise, different experts. ▸ bridging knowledge gap through probabilistic graphical model.

Motzek et al. (2015)

▸

and are creatable directly and independently and by experts. Great! ...?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Models

BF1 BF2 BP1 CM1 A B BF2 BF3 BF4 BP2 CM1 A B C D

Lorem ipsum dolor sit amet, consetetur sadip- scing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam non- umy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea.

▸ obtained directly from multiple experts. ▸ automatically extracted from multiple BPMNs. ▸ reconstructed from literal descriptions. ▸ Local views, multiple models.

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Models

BF1 BF2 BP1 CM1 A B BF2 BF3 BF4 BP2 CM1 A B C D

Lorem ipsum dolor sit amet, consetetur sadip- scing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam non- umy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea.

▸ obtained directly from multiple experts. ▸ automatically extracted from multiple BPMNs. ▸ reconstructed from literal descriptions. ▸ Local views, multiple models. ▸ But... one consistent model required.

→ match & merge.

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Models

BF1 BF2 BP1 CM1 A B BF2 BF3 BF4 BP2 CM1 A B C D

Lorem ipsum dolor sit amet, consetetur sadip- scing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam non- umy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea.

▸ obtained directly from multiple experts. ▸ automatically extracted from multiple BPMNs. ▸ reconstructed from literal descriptions. ▸ Local views, multiple models. ▸ But... one consistent model required.

→ match & merge.

▸ classical business process or ontology matching

problem?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Classical Problem?

▸

= different nomenclatures, languages, abbreviations

▸ English & Italian ▸ High Level Voltage Control and Distribution = HCD = HLV CD ▸ xuel ≠ muel ≠ ferp ≠ mferp ▸ dorete = 192.18.210.7 = 02-00-D0-12-C7-93 = 718be323-9d58-4ada-9629-81a6f42a9703

→ linguistic approaches of no avail.

▸ flow ⇔ dependencies

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Model Merging and Normalization

same dependencies, same entity. = sub-graph isomorphism problem

(but much easier)

DAG, labeled leaves, known topo-order.

▸ exploit dependency structures. ▸ exploit references by external sources (inventories) ▸ match, normalize & merge

(almost) linear in number of nodes

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Business Dependency Model Merging and Normalization

▸ we build structure identifying coding

e.g., 2- 1-A 3-ABC

▸ naturally sorted, inexpensive lookup ▸ allows for partial matching

CD ABC DEF matches Q ABC CDE DEG by DEF: F→G, +Q, CD:+E

▸ sequence alignment ▸ postponed linguistic sufficiency checking

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B

  • 1. encode CM1

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B

  • 1. encode CM1

2. encode BP1

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B

  • 1. encode CM1

2. encode BP1 3. encode BF1

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X 01-X

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X 01-X

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X 01-X

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X Y 01-X

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2 8. lookup B → ID Y

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BF2 BP1 CM1 A B X Y 01-X 02-X Y

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2 8. lookup B → ID Y 9. code BF2 = 02-X Y

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BFq BP1 CM1 A B X Y 01-X 02-X Y

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2 8. lookup B → ID Y 9. code BF2 = 02-X Y 10. lookup 02-X Y → merge BFq

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BFq BP1 CM1 A B X Y 01-X 02-X Y 02- 01-X 02-X Y

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2 8. lookup B → ID Y 9. code BF2 = 02-X Y 10. lookup 02-X Y → merge BFq 11. code BP1 = 02- 01-X 02-X Y

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Simple Example

known ⃗ BF: BF1 = 01-X BFq = 02-X Y BFr = 03-V Y Z BFs = 03-X Y Z known ⃗ BP: BP1 = 02- 01-X 02-X Y BPh = 02- 02-X Y 03-V Y Z BPg = 02- 03-X Y Z 03-V Y Z

BF1 BFq BP1 CM1 A B X Y 01-X 02-X Y 02- 01-X 02-X Y

  • 1. encode CM1

2. encode BP1 3. encode BF1 4. lookup A → ID X 5. code BF1 = 01-X 6. lookup 01-X → new 7. encode BF2 8. lookup B → ID Y 9. code BF2 = 02-X Y 10. lookup 02-X Y → merge BFq 11. code BP1 = 02- 01-X 02-X Y 12. lookup 02- 01-X 02-X Y → new lookup: binary search, hashmap. option: partial lookup

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Conclusion and Future Work

▸ BDMs compactly represent dependencies of companies on complex infrastructures from multiple

perspectives

▸ directly and independently understandable and creatable by multiple experts ▸ linearly scaling match and merge ▸ may assist Business Process Model Matching

transforming BPMN to BDM is straightforward matching BDM likely represents matching BPMN

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16

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INSTITUTE OF INFORMATION SYSTEMS

Thank You.

... questions?

MOTZEK ET AL. SEMANTIC NORMALIZATION AND MATCHING OF BUSINESS DEPENDENCY MODELS, CBI’16