We consider the problem of numerical stability and model density growth when training a sparse linear model from massive data. We focus on scalable algorithms that optimize certain...
The queueing Petri net (QPN) paradigm provides a number of benefits over conventional modeling paradigms such as queueing networks and generalized stochastic Petri nets. Using qu...
Web-services are broadly considered as an effective means to achieve interoperability between heterogeneous parties of a business process and offer an open platform for developing...
We have recently shown how use cases can be systematically transformed into UML state charts considering all relevant information from a use case specification, including pre- and ...
Computational fluid dynamics (CFD) of complex processes and complicated geometries embraces the transport of momentum, heat, and mass including the description of reaction kinetic...