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The problem of prediction is an important task in network analysis, which has attracted more attention from computer science communities. In this paper, prediction in signed heterogeneous networks is addressed, which contains two aspects, link prediction and sign prediction. Most of previous studies focus on non-signed networks that have only positive links or homogeneous networks that have only one...
Relevance search in heterogeneous information networks is a basic and crucial operation which is usually used in recommendation, clustering and anomaly detection. Nowadays most existing relevance search methods focus on objects in homogeneous information networks. In this paper, we propose a method to find the top-k most relevant objects to a specific one in heterogeneous networks. It is a two phase...
We develop a method for matching unstructured reviews to database objects in data integration, where each object has a set of attributes. To this end, we propose a Labeled Latent Dirichlet Allocation model. We model reviews as if they were generated by a two-stage stochastic process. Each review is represented by a probability distribution over attributes, and each attribute is represented as a probability...
MapReduce is emerging as an important programming model for massive data processing. A key challenge in MapReduce environments is the ability to efficiently control resource allocation and task scheduling for achieving Service Level Objectives (SLOs) of MapReduce jobs. However, there are few effective task scheduling methods to guarantee MapReduce jobs' SLOs. Therefore, we address this challenge by...
In a variety of domains, the amount of information grows rapidly; new sources and types of information are proliferating. In recent years, the world-wide web information has been growing at a dramatic pace and might have become the most important information source for most people. In daily life, when people encounter some problems, they tend to retrieve the information from the Web search engines...
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