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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 3 1 Performance of a kernel Fisher discriminant function on a 2d feature space 2 Neural network model perfor- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Experiments 2 Parameter 3 2 P a ar m et er 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 ■■ dynamic non-linear systems respect to their significance? (Customer opinion analysis) ■■ Development and case specific adaptation of data based models ■■ ■■ How to achieve statistically reliable forecasts of turnovers, correlations between Analysis and validation of series of measurements ■■ Realization of simulations and evaluation of results turnovers, special marketing activities, ■■ 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 ■■ Used programming languages and simulation instruments MATLAB, R, C++, Java, how can they be quantified? ABAQUS, etc. Software development ■■ Development and implementation of individual modules for data based system Methods identification and decision support instruments ■■ System theory (non-linear dynamic models, Hysteresis Models) ■■ Integration of developed modules into existing simulation and analysis tools ■■ Machine learning (feed-forward and recurrent neural networks) ■■ Allocation of possibilities for reidentifi cation and adaptation of developed ■■ 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 ■■ Data Mining (Classification, Clustering, Significance analysis, Spatial Data Mining, Regression) ■■ 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