Unveiling Design Patterns: A Visual Guide with UML Diagrams
Machine Data Integration Framework Maps Data for Systems
1. A Mapping-Based Framework for
the Integration of Machine Data
and Information Systems
Heiko Kern*, Fred Stefan*, Vladimir Dimitrieskiᵀ, Klaus-Peter Fähnrich*
* University of Leipzig, Germany
ᵀ University of Novi Sad, Serbia
8th IADIS International Conference on Information Systems
Madeira, Portugal, 16.03.2015
2. Intelligent
Integration
Automation of production
Continuous information flow between factory and enterprise
level
Quality management
Production planning
Increasing production efficiency
Motivation
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Factory level
Enterprise level
… …
MES
QMSPLS
PPS
……
Cloud services
…Storage
3. Problem
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Development of connectors
Heterogeneity of data
structures
Transformation of data
Hard-coded transformations
Error-prone and costly
No portability of solution
knowledge
Scenario 1: Set-up costs of
manufacturing execution
systems
Scenario 2: Change of
production process -> change
of integration
Service Bus
Connector
Machine A
Connector
Machine C
Connector
Machine B
Connector
IS
Connector
Machine A
Connector
Machine C
Connector
Machine BConnector
Machine A
Connector
Machine C
Connector
Machine B
4. Objective
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Improve the development of
connectors
Structured development
Explicit description of
transformation knowledge
Reuse of transformations
Automatic creation of
connectors
Research focus
Transformation description
Diversity of data
Reuse of transformations
Research method
Design Science
Service Bus
Mapping-based
Integration Framework
Connector
IS
6. Mapping Framework
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Machine
data
(e.g. CSV)
Data
schema
Data
Source Target
Element
tree
Information
system
(e.g. XML)
Data
schema
Data
Integration platform
Data
schema
Data
Element
tree
Data
schema
Data
Mapper
Mapping
Generator
Data
transformation
Binding Binding
Instance
of
Instance
of
Mapping
Repository
Reuse
algorithms
7. Representation of Data Schemas
Binding Concept
Representation as tree
View on data schemas
References on elements in data schema
Binder for each data schema technology
Examples
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ElementContainer
Element
0..*elements
1
0..* children
parent
8. Mapping Description
Mapping Language
Declarative, graphical, abstraction from transformation execution
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Mapping
Container
NodeLink
FunctionConstantValue
1
sources
1
1
targets
*
nodes
ZeroToAny
OneToMany
ManyToMany
Operator
ManyToOne
OneToOne
0..*
links
0..1
dependsOn
ElementContainer
Element
0..*elements
1
0..* children
parent
1..*
element containers
10. Transformation Execution
Generator approach
For each combination of
Execution environment
Source schema technology
Target schema technology
Platform-independence enables the portability to different
execution environments
Transformation systems
XSLT
Programming languages
Java, C#
Integration platforms
MuleESB
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11. Mapping Repository and Reuse Algorithms
Storage of mappings in repository as knowledge base
Reuse approach
Comparison -> potential rule candidates
Adaption -> from repository rules to new rules
Application of rules -> construction of complete mapping
Comparison
Different approaches: syntax, semantic, structure
Combination of comparators
Degree of automated reuse
Suggestions during design time in editor
Fully automatic during run-time in execution environment
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16. Evaluation
Different use cases
CSV, XML, OPC, SECS/GEM
Mapping language
Mapping language is suitable in these use cases
But: definition of fine-grained expressions (e.g. conditions,
queries/navigation)
Graphical representation fits to the skills of a modeler
But: many mapping lines are confusing
Reuse and automatic creation of mappings
Semi-automatic reuse works
But: Automatic reuse is a challenging tasks
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