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

Heiko Kern*,  Fred Stefan*,  Vladim...
Motivation

Automation of production

Cloud services

     
 

Enterprise level

 
 

Factory level

Continuous informatio...
Problem

 

Connector
IS
Service Bus
cnnnnhfnr cnnnnnrnr (‘nnnnnmr
r (‘nnnnnfnr r Cnnnnhfnr I (‘nnnmmr
I Connector I Conne...
| Improve the development of
connectors

:1 Structured development

 W.  g :1 Explicit description of
"‘ ‘ " T " transform...
The Integration Approach
Mapping Framework

Source

Machine
data
(e. g. CSV)

Data
schema

Target

Information

system
(e. g. XML)

Data
schema

Ma...
Representation of Data Schemas

I Binding Concept

> Representation as tree

> View on data schemas

> References on eleme...
Mapping Description

I Mapping Language
> Declarative,  graphical,  abstraction from transformation execution

  

1.. *
_...
l Mapping Description

Mapper editor

File Edit Reusability

Ell:  

4 CSVfile 4 XSDfi| e
4 Rows  - 4 JSCharl: 
No  4 data...
Transformation Execution

Generator approach

t1 For each combination of
F Execution environment
F Source schema technolog...
Mapping Repository and Reuse Algorithms

I Storage of mappings in repository as knowledge base

I Reuse approach
> Compari...
Use Case
Use Case:  Wafer Thickness Measurement

Curved matenal
(water etc. )

  
  
 
   

GS-3813

Linear gauge sensor AX_5022 RS...
Single-Layer Measurement

Definition of mapping rules
Code generation and execution
Storage in repository -> learning phas...
Double-Layer Measurement

Sensor -> Sensor_A and Sensor_B
Automatic rule application
Code generation and execution

Mapper...
Evaluation

Different use cases
tv CSV,  XML,  OPC,  SECS/ GEM

Mapping language
tr Mapping language is suitable in these ...
Thank You. 
Questions?
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A Mapping-Based Framework for the Integration of Machine Data and Information Systems

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

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

  1. 1. A Mapping-Based Framework for the Integration of Machine Data and Information Systems Heiko Kern*, Fred Stefan*, Vladimir DimitrieskiT, Klaus—Peter Fahnrich* * University of Leipzig, Germany T University of Novi Sad, Serbia 8th IADIS International Conference on Information Systems Madeira, Portugal, 16.03.2015
  2. 2. Motivation Automation of production Cloud services Enterprise level Factory level Continuous information flow between factory and enterprise level : ' Quality management : » Production planning :1 Increasing production efficiency sth IADIS International conference on Information Systems M
  3. 3. Problem Connector IS Service Bus cnnnnhfnr cnnnnnrnr (‘nnnnnmr r (‘nnnnnfnr r Cnnnnhfnr I (‘nnnmmr I Connector I Connector I Connector Machine A Machine B Machine C K. Q97 . ,5 _ 8th IADIS International conference on Information Systems Development of connectors : Heterogeneity of data structures ‘a Transformation of data ’» Hard—coded transformations ‘I Error—prone and costly ‘» No portability of solution knowledge v v v v Scenario 1: Set-up costs of manufacturing execution systems Scenario 2: Change of production process -> change of integration on
  4. 4. | Improve the development of connectors :1 Structured development W. g :1 Explicit description of "‘ ‘ " T " transformation knowledge :1 Reuse of transformations Connector :1 Automatic creation of IS connectors Service Bus M ' -b d . meg; ‘:I’J2%: ;:wo. k Research f°C"S :1 Transformation description :1 Diversity of data :1 Reuse of transformations 3.’ ll , ;i>_ . ' Ix 1 - . '3.. » Q -"1/' ‘ Research method :1 Design Science sth IADIS International conference on Information Systems .5
  5. 5. The Integration Approach
  6. 6. Mapping Framework Source Machine data (e. g. CSV) Data schema Target Information system (e. g. XML) Data schema Mapping Reuse Repository ‘ algorithms Element Ma erg] Element tree pp tree Binding i Binding Mapping Schema 1 SCherna. Instance Generatol“ of Data Data ed Data I transformation Integration platform Data sth IADIS International Conference on Information Systems at
  7. 7. Representation of Data Schemas I Binding Concept > Representation as tree > View on data schemas > References on elements in data schema > Binder for each data schema technology I Examples Elementcontainer 4 CSV file 4 Rows MA-Nr. Nr. Rub Rus Rtk Us vor Us nach Ausfall 4 XSDfi| e 4 JSChart 4 dataset id WPE 4 data value unit 4 optionset 4 option set value 4 SECS Message 4 SECS/ GEM Message 4 LB] U4 42 U4 Wafertiompleted 4 L[1] 4 L[2] U4 HostDefinedReport_28 4 L[6] A 2014081913480000 A "null" A Passivierungsmessung A PAD00022 A 0 I L[5] sth IADIS International Conference on Information Systems ~l
  8. 8. Mapping Description I Mapping Language > Declarative, graphical, abstraction from transformation execution 1.. * _ 0neTo0ne ElementContainer 0--* ManyTo0ne Mapping Container links 0__* sources 1 targets 1 parent 0.. * children M3"YT°M3nY 0..1 Constantvalue E Operator f one-romany dependson ZeroToAny 8th IADIS International Conference on Information Systems
  9. 9. l Mapping Description Mapper editor File Edit Reusability Ell: 4 CSVfile 4 XSDfi| e 4 Rows - 4 JSCharl: No 4 dataset Sensor Property Value 8th IADIS International Conference on Information Systems
  10. 10. Transformation Execution Generator approach t1 For each combination of F Execution environment F Source schema technology n Target schema technology Platform-independence enables the portability to different execution environments t1 Transformation systems F XSLT t1 Programming languages F Java, C# t1 Integration platforms F Mu| eESB 8th IADIS International Conference on Information Systems <s>—
  11. 11. Mapping Repository and Reuse Algorithms I Storage of mappings in repository as knowledge base I Reuse approach > Comparison —> potential rule candidates > Adaption —> from repository rules to new rules > Application of rules —> construction of complete mapping I Comparison > Different approaches: syntax, semantic, structure > Combination of comparators I Degree of automated reuse > Suggestions during design time in editor > Fully automatic during run—time in execution environment sth IADIS International conference on Information Systems —I: .‘l; ./
  12. 12. Use Case
  13. 13. Use Case: Wafer Thickness Measurement Curved matenal (water etc. ) GS-3813 Linear gauge sensor AX_5022 RS232C cable Curved matenal <w= _=: er_em DG-5100 1 Digital gauge counter User preparation Gauge stand with lens stage powe. -came (user preparation) sth IADIS International conference on Information Systems
  14. 14. Single-Layer Measurement Definition of mapping rules Code generation and execution Storage in repository -> learning phase Mapper editor ': l @ V File Edit Reusability 4 CSVfi| e 4 XSDfi| e 4 Rows 4 JSChart No 4 damsel’ CSV iensor XML <JSChart> Id Sensor <dataset id= "Rub" 0 35.3 type= "line"> 1 344 <data unit= "O" ' value= "35.3"/ > 2 34.6 <data unit= "1" 3 35_1 value= "34.4"/ > 4 35.1 / d"' < ataset> 5 37-1 </ JSChart> 14 8th IADIS International Conference on Information Systems —
  15. 15. Double-Layer Measurement Sensor -> Sensor_A and Sensor_B Automatic rule application Code generation and execution Mapperedkor Ffle Edk Reusabflky I1o—vA CSV ' XML ‘ X5 <JSChart> ‘ <dataset id= "Rub_B" Id Sensor_A Sensor_B ,0, A tYpe= '_'h“e"> 0 10 12 - <data un1t= "O" 5°’-B value= "34 . 9" / > 1 15 13 <data unit= "1" 2 12 12 value= "35.0"/ > 3 14 11 m 4 11 23 </ dataset> 5 11 23 <dataset id= "Rub_A" 6 13 22 type= "line"> 7 14 11 <data unit= "O" 8 value= "21.8"/ > 9 12 11 <data unit= ”1" value= "1.2"/ > 10 13 12 </ dataset> </ JSChart> '1 Prooerrv value 3 Pronertv Value 1 Prooertv value - 1' 15 8th IADIS International Conference on Information Systems _
  16. 16. Evaluation Different use cases tv CSV, XML, OPC, SECS/ GEM Mapping language tr Mapping language is suitable in these use cases P But: definition of fine-grained expressions (e. g. conditions, queries/ navigation) tv Graphical representation fits to the skills of a modeler P But: many mapping lines are confusing Reuse and automatic creation of mappings t» Semi—automatic reuse works F But: Automatic reuse is a challenging tasks 14th Workshop on Domain-Specific Modeling <= >—
  17. 17. Thank You. Questions?

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