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F R A U N H O F E R I N S T I T U T e F o R I ndustria l mathematics
3
Common Prob. Density
Non−Linear Model Performance on validation data set
2.5
2
2
Quality parameter
1
0
1.5
1
0.5
−1
Experimgents
Model output
95% Confidence intervall
0
−0.5
−2
1
−3
−2
−1
0
1
2
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1 Performance of a kernel Fisher
discriminant function on a 2d feature space
2 Neural network model perfor-
1
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9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
Experiments
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Parameter
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Business and Production
Process Analytics via Data
Mining
mance on the validation data set
3 2d parameter probability
Problem
tion of those prognosis models with inter-
­density estimation via Markov
Chain Monte Carlo sampling
tion of the system behaviour. The combina-
Data Warehouse processes are already im-
active visual navigation tools leads to highly
plemented in many enterprises and are be-
efficient multicriterial decision support
longing to this enterprises as a commodity.
tools.
On the other hand the business intelligence
solutions, which are based on information
provided by the Data Warehouses, are not
The challenge
an integral part of industrial companies.
The challenge lies in providing high reliable
Fraunhofer-Institut für Techno- und
Wirtschaftsmathematik ITWM
The high, and growing amount of collected
answers to the following questions:
data, (which in many cases is complex
struc­tured and time dependent) is available
■■
How to improve the product quality res-
Fraunhofer-Platz 1
for knowledge extraction and decision sup-
pectively product performance, with mi-
67663 Kaiserslautern
port purposes. Furthermore it is known
nimal costs?
that more than 75 % of the collected data
Contact
have spatial references, which are dynami-
Dr. Alex Sarishvili
cally changing.
■■
How to produce economically optimal
products?
Telefon +49 631 31600-46 83
[email protected]
Prognosis models that we identify considering possible disturbance sources and sys-
www.itwm.fraunhofer.de
© Fraunhofer ITWM 2010
tem uncertainties allow the reliable predic-
■■
How to simulate interesting processes,
under varying process parameters?
1
·Forecast customer demand
·Predict product performance
·Customers data analysis
·Product market analysis
·Mine call center records
·Spatial correlation of facilities
·Identify trends
·Simulate behaviour
·…
·Data warehouse exploration
·Standard statistics
·Data generation
·Feature extraction from
non-structured data sources
Problem formulation
Data acquisition
■■
Data preprocessing
·Design of Experiments
·Dimension reduction
·Outlier identification
System identification
Gray Box
Model
·…
Who and where are top-selling custo-
Data preprocessing
Performance/Quality
Data acquisition
Feature vector
Problem formulation
Inference
·Prediction
·Simulation
·Decision support
·Knowledge extraction
·…
System identification
We offer
Inference
Our competencies
mers and under which features can they
be grouped? (Customer data analysis)
■■
Modeling and Simulation
How can customer call center recalls be
■■
automatically grouped and analized with
Design of Experiments, consulting and
­implementation
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dynamic non-linear systems
respect to their significance? (Customer
opinion analysis)
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Development and case specific adaptation of data based models
■■
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How to achieve statistically reliable forecasts of turnovers, correlations between
Analysis and validation of series of measurements
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Realization of simulations and evaluation
of results
turnovers, special marketing activities,
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Estimation and optimization of process
parameters
and market trends?
■■
■■
Modeling and identification of static and
Decision support via the optimization of
Are there seasonal or spatial correlations
control parameters on the basis of the
between variables of interest? And if yes
identified models
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Used programming languages and simulation instruments MATLAB, R, C++, Java,
how can they be quantified?
ABAQUS, etc.
Software development
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Development and implementation of
­individual modules for data based system
Methods
identification and decision support instruments
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System theory (non-linear dynamic models, Hysteresis Models)
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Integration of developed modules into
­existing simulation and analysis tools
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Machine learning (feed-forward and recurrent neural networks)
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Allocation of possibilities for reidentifi­
cation and adaptation of developed
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Mathematical Statistics (Maximum Likelihood, Bayesian Approach, Monte Car-
­models
lo Methods: Markov Chain Monte Carlo,
Development of new solutions
■■
­Sequential Monte Carlo)
Collaboration in national and international donor projects in a network with industrial partners
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Data Mining (Classification, Clustering,
Significance analysis, Spatial Data Mining,
Regression)
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Development of new approaches for data
based system identification, simulation, prognosis and optimization of control
and process parameters
AS_Flyer_Business and Production process
■■
Data based modeling and system identification