Business-process reengineering (BPR) is a generic term covering a variety of perspectives on how to change organizations. There are at least two distinct roles for AI in BPR. One role is as an enabling technology for reengineered processes. A second, less common but potentially important role is in tools to support the change process itself.
The goal of the Process Handbook project is to provide a set of theories, methodologies, and tools, to enable the modeling and redesign of organizations in a more systematic way. A key element of the work is a novel approach to representing processes, which uses ideas from computer science about inheritance, and from coordination theory about managing dependencies. This representation improves understanding of complex processes, assists in the identification of process inefficiencies, and facilitates generation and comparative evaluation of alternative processes. We have built an online Process Handbook computer tool based on our approach, to represent, store, classify and manipulate business processes. Using that tool, we have developed the beginnings of a systematic design method for process (re)design.
The Hawkes process has been widely applied to modeling self-exciting events, including neuron spikes, earthquakes and tweets. To avoid designing parametric kernel functions and to be able to quantify the prediction confidence, non-parametric Bayesian Hawkes processes have been proposed. However the inference of such models suffers from unscalability or slow convergence. In this paper, we first propose a new non-parametric Bayesian Hawkes process whose triggering kernel is modeled as a squared sparse Gaussian process. Second, we present the variational inference scheme for the model optimization, which has the advantage of linear time complexity by leveraging the stationarity of the triggering kernel. Third, we contribute a tighter lower bound than the evidence lower bound of the marginal likelihood for the model selection. Finally, we exploit synthetic data and large-scale social media data to validate the efficiency of our method and the practical utility of our approximate marginal likelihood. We show that our approach outperforms state-of-the-art non-parametric Bayesian and non-Bayesian methods.
If you've been using Windows for a while, there's a good chance you've had to use the built-in Task Manager at some point or another. Whether it's to kill a frozen process, track down some nasty malware, or figure out what's eating up all that memory, the Task Manager is an invaluable tool for any intermediate or advanced user. But for enthusiasts that want extra control, more information, and a host of extra features, there's a more powerful alternative available: Microsoft's free Process Explorer tool. It also includes the ability to sniff out viruses and identify when programs are clinging to software you want to delete. Part of the Sysinternals suite of Windows tools (formerly "Winternals"), Process Explorer can be downloaded from TechNet a la carte or as part of the entire suite.
Hi, I am making a tutorial/jupyter notebook on Gaussian Process Regression. I just finished part 1. My intention is to help people understand the algorithm without getting to deep into the mathematics / bayesian statistics. It gives an introduction and lists some pros/cons for GPRegression. Then presents the math of gaussian processes and teaches how to sample a gaussian process in numpy.