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

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 SystemsMadeira, Portugal, 16.03.2015

Intelligent Integration

Automation of production

Continuous information flow between factory and enterprise level Quality management Production planning Increasing production efficiency

Motivation

8th IADIS International Conference on Information Systems2

Factory level

Enterprise level

… …

MES

QMSPLS

PPS

……

Cloud services

…Storage

Problem


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

ConnectorIS

Connector Machine A

Connector Machine C

Connector Machine BConnector

Machine AConnector Machine C

Connector Machine B

Objective


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

The Integration Approach

Mapping Framework


Machine data

(e.g. CSV)

Data schema

Data

Source TargetElement

treeInformation

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

MappingRepository

Reuse algorithms

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


ElementContainer

Element

0..*elements

1

0..* children

parent

Mapping Description

Mapping Language Declarative, graphical, abstraction from transformation

execution


MappingContainer

NodeLink

FunctionConstantValue

1sources

1

1targets

*

nodes

ZeroToAny

OneToMany

ManyToMany

Operator

ManyToOne

OneToOne

0..*

links

0..1

dependsOn

ElementContainer

Element

0..*elements

1

0..* children

parent

1..*element containers

Mapping Description


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


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


Use Case

Use Case: Wafer Thickness Measurement


Definition of mapping rules Code generation and execution Storage in repository -> learning phase

Single-Layer Measurement


XML

<JSChart> <dataset id="Rub" type="line"> <data unit="0" value="35.3"/> <data unit="1" value="34.4"/>

… </dataset></JSChart>

CSV

IdSensor

0 35.3

1 34.4

2 34.6

3 35.1

4 35.1

5 37.1

Double-Layer Measurement

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


CSV

Id Sensor_A Sensor_B0 10 121 15 132 12 123 14 114 11 235 11 236 13 227 14 118 15 139 12 1110 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>

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

14th Workshop on Domain-Specific Modeling16

Thank You.Questions?

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

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