Titlebook: Investment, R&D, and Long-Run Growth; Dietmar Hornung Book 2002 Springer-Verlag Berlin Heidelberg 2002 Investment.economic growth.growth.g

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Morbid

ut by any process mining technique. This notion is related to the well-known OLAP (Online Analytical Processing) data cubes, adapting the OLAP paradigm to event data through .. This adaptation is far from trivial given the nature of event data which cannot be easily summarized or aggregated, conflic

Forsake

Dietmar Hornungut by any process mining technique. This notion is related to the well-known OLAP (Online Analytical Processing) data cubes, adapting the OLAP paradigm to event data through .. This adaptation is far from trivial given the nature of event data which cannot be easily summarized or aggregated, conflic

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Dietmar Hornungexploring, evaluating, and enhancing modeling methods and methodologies for the analysis and design of information systems, enterprises, and business processes. They are grouped into topical sections on fundamental issues in modeling, requirements and regulations, enterprise?and software ecosystem m

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Dietmar Hornung), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.

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Dietmar Hornung), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.

finale

), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.

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PAC

Dietmar Hornung), the discovery of social networks in a process context becomes truly scalable. The approach has been implemented using the open source process mining toolkit ProM. The experiments reported in this paper demonstrate scalability while providing results instantly.