ProFiDo - The Processes Fitting Toolkit Dortmund

This paper describes the Java-based toolkit ProFiDo which integrates several tools for fitting input models. Currently supported are command line tools for fitting probability distributions, ARIMA processes and Markovian arrival processes. The toolkit provides a graphical user interface which allows for the specification of workflows that describe the different steps of data preprocessing, parameter fitting and result visualization. The basis for the interoperability of the different tools is an XML based interchange format for the specification of various types of processes. An XML based configuration file supports the extension of the toolkit by integrating additional fitting methods or analysis approaches.

[1]  Marcel F. Neuts,et al.  Matrix-Geometric Solutions in Stochastic Models , 1981 .

[2]  M. Neuts A Versatile Markovian Point Process , 1979 .

[3]  Barry L. Nelson,et al.  Autoregressive to anything: Time-series input processes for simulation , 1996, Oper. Res. Lett..

[4]  C. O'Cinneide On non-uniqueness of representations of phase-type distributions , 1989 .

[5]  Rudolf Hornig,et al.  An overview of the OMNeT++ simulation environment , 2008, Simutools 2008.

[6]  Peter Buchholz,et al.  An EM-Algorithm for MAP Fitting from Real Traffic Data , 2003, Computer Performance Evaluation / TOOLS.

[7]  Sally Floyd,et al.  Wide area traffic: the failure of Poisson modeling , 1995, TNET.

[8]  Randall P. Sadowski,et al.  Simulation with Arena , 1998 .

[9]  Peter Buchholz,et al.  A Novel Approach for Phase-Type Fitting with the EM Algorithm , 2006, IEEE Transactions on Dependable and Secure Computing.

[10]  John M. Chambers,et al.  Software for Data Analysis: Programming with R , 2008 .

[11]  Ren Asmussen,et al.  Fitting Phase-type Distributions via the EM Algorithm , 1996 .

[12]  Peter Buchholz,et al.  A Heuristic Approach for Fitting MAPs to Moments and Joint Moments , 2009, 2009 Sixth International Conference on the Quantitative Evaluation of Systems.

[13]  Gábor Horváth,et al.  A minimal representation of Markov arrival processes and a moments matching method , 2007, Perform. Evaluation.

[14]  Barry L. Nelson,et al.  Fitting Time-Series Input Processes for Simulation , 2005, Oper. Res..

[15]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[16]  András Varga,et al.  An overview of the OMNeT++ simulation environment , 2008, SimuTools.

[17]  Evgenia Smirni,et al.  KPC-Toolbox: Simple Yet Effective Trace Fitting Using Markovian Arrival Processes , 2008, 2008 Fifth International Conference on Quantitative Evaluation of Systems.

[18]  Peter Buchholz,et al.  An Empirical Comparison of MAP Fitting Algorithms , 2010, MMB/DFT.

[19]  Peter Buchholz,et al.  A MAP fitting approach with independent approximation of the inter-arrival time distribution and the lag correlation , 2005, Second International Conference on the Quantitative Evaluation of Systems (QEST'05).

[20]  Barry L. Nelson,et al.  Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Processes , 1998, INFORMS J. Comput..

[21]  C. O'Cinneide Phase-type distributions: open problems and a few properties , 1999 .

[22]  Miklós Telek,et al.  PhFit: A General Phase-Type Fitting Tool , 2002, Computer Performance Evaluation / TOOLS.

[23]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[24]  Averill M. Law,et al.  How the Expertfit distribution-fitting software can make your simulation models more valid , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[25]  John M. Chambers,et al.  Software for data analysis , 2008 .

[26]  C. Pipper,et al.  [''R"--project for statistical computing]. , 2008, Ugeskrift for laeger.