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In this paper, we focus on distance-based outliers detection in an uncertain dataset, which is very useful in large social network. Based on the x-tuple model and the possible world semantics, we propose the concept of tuple outlier score, top k\ probability and top (k1, k2) distance-based outlier. We then design an algorithm using dynamic programming technique to calculate tuple outlier scores and...
Microblogs have become an important platform for people to publish, transform information and acquire knowledge. This paper focuses on the problem of discovering user interest in microblogs. In this paper, we propose a topic mining model based on Latent Dirichlet Allocation (LDA) named user-topic model. For each user, the interests are divided into two parts by different ways to generate the microblogs:...
Large-scale distributed monitor networks are in face of the challenge of tremendous data communication costs due to the resource restriction. Prediction models can be used to reduce communication cost over the networks. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still...
Processing multi-extreme value queries efficiently over data streams is important for data analysis in real-time environment. Cost-efficient processing of continuous extreme values queries over sliding windows, especially about resource sharing, is considered. Firstly, an effective storage structure to minimize the number of elements to be kept for queries is given. We prove the average the cardinality...
Conficker is a worm outbreak recently which form a large botnet and became a huge threat to the security of the internet. In this paper, the redirect technology of domain name was used to monitor the conficker. For it's low killing rate and long-term period of propagation, we built a propagation model of botnet based on conficker monitoring. In the model, we take into account the geography, connectivity...
Large-scaled distributed monitoring systems are in face of the challenge of massive data and resource restriction. Prediction models can be used to reduce communication cost over the Net. A framework is proposed which provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision...
Feature selection is among the keys in many applications, especially in mining high-dimensional data. With lack of labeled instances, the learning accuracy may deteriorate using traditional methods. In this paper, we introduce a ldquowrapperrdquo type semi-supervised feature selection approach based on RSC model. It extends the class label from labeled training set to unlabeled data. Additionally,...
This paper considers the problem of the dynamic risk assessment for the network based on the threat stream analysis. We analyze the general approach to do the network dynamic risk assessment. A stream based cube model is built to analyze the characteristics of the threat stream. Then combining with the research about the description and analysis of the threat effect, we propose the architecture of...
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