New application systems generated by composition of web services dynamically have become a development trend in network environment.However, since a variety of external services are invoked with different quality of service during processing,the problem of how to keep the execution stable must be addressed in order to improve the reliability and availability of the combination of services.In th...

We deene a context-sensitive temporal probability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a Bayesian network construction algorithm whose generated networks give sound and complete answ...

Bayesian networks are graphical statistical models that represent inference between data. For their effectiveness and versatility, they are widely adopted to represent knowledge in different domains. Several research lines address the NP-hard problem of Bayesian network structure learning starting from data: over the years, the machine learning community delivered effective heuristics, while di...

We define a context-sensitive temporal prob­ ability logic for representing classes of discrete-time temporal Bayesian networks. Context constraints allow inference to be focused on only the relevant portions of the probabilistic knowledge. We provide a declarative semantics for our language. We present a Bayesian network construction algorithm whose generated networks give sound and complete a...

In the era of big data and Internet of things, massive sensor data are gathered with Internet of things. Quantity of data captured by sensor network are considered to contain highly useful and valuable information. However, since sensor data are usually correlated in time and space, not all the gathered data are valuable for further data processing and analysis. Preprocessing is necessary for e...

Algorithms for probabilistic inference in Bayesian networks are known to have running times that are worst-case exponential in the size of the network. For networks with a moralised graph of bounded treewidth, however, these algorithms take a time which is linear in the network’s size. In this paper, we show that under the assumption of the Exponential Time Hypothesis (ETH), small treewidth of ...

This paper presents a new hybrid approach for learning Bayesian networks (BNs) based on artificial bee colony algorithm and particle swarm optimization. Firstly, an unconstrained optimization problem is established, which can provide a smaller search space. Secondly, the definition and encoding of the basic mathematical elements of our algorithm are given, and the basic operations are designed,...

Given evidence on a set of variables in a Bayesian network, the most probable explanation (MPE) is the problem of finding a configuration of the remaining variables with maximum posterior probability. This problem has previously been addressed for discrete Bayesian networks and can be solved using inference methods similar to those used for finding posterior probabilities. However, when dealing...

Data clustering, including problems such as finding network communities, can be put into a systematic framework by means of a Bayesian approach. Here we address the Bayesian formulation of the problem of finding hypergraph communities. We start by introducing a hypergraph generative model with a built-in group structure. Using a variational calculation we derive a variational Bayes algorithm, a...

In this paper, we present an algorithm for learning structures of Bayesian models in multiple projection spaces. We assume that a visual phenomenon can be projected on a set of spaces that share a common subspace. We propose that models of individual projections can be related through probability distributions over the shared subspace. We develop a learning method that estimates simultaneously ...