A Mapping-Based Framework for the Integration of Machine Data and Information 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

5. The Integration Approach

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

9. Mapping Description
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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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12. Use Case

13. Use Case: Wafer Thickness Measurement
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14.  Definition of mapping rules
 Code generation and execution
 Storage in repository -> learning phase
Single-Layer Measurement
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XML
<JSChart>
<dataset id="Rub"
type="line">
<data unit="0"
value="35.3"/>
<data unit="1"
value="34.4"/>
…
</dataset>
</JSChart>
CSV
Id Sensor
0 35.3
1 34.4
2 34.6
3 35.1
4 35.1
5 37.1

15. Double-Layer Measurement
 Sensor -> Sensor_A and Sensor_B
 Automatic rule application
 Code generation and execution
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CSV
Id Sensor_A Sensor_B
0 10 12
1 15 13
2 12 12
3 14 11
4 11 23
5 11 23
6 13 22
7 14 11
8 15 13
9 12 11
10 13 12
XML
<JSChart>
<dataset id="Rub_B"
type="line">
<data unit="0"
value="34.9"/>
<data unit="1"
value="35.0"/>
…
</dataset>
<dataset id="Rub_A"
type="line">
<data unit="0"
value="21.8"/>
<data unit="1"
value="1.2"/>
…
</dataset>
</JSChart>

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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17. Thank You.
Questions?