Yanjun Shu

A tree-based reliability analysis for fault-tolerant web services composition

Reliability is critical for choosing, ranking and composing Web services. However, some common situations, such as fault-tolerant strategies and the dynamic operational profile, are not considered in existing reliability analysis. To solve these problems, a tree-based composition structure model is proposed, which is called the Fault-tolerant Composite Web Services Tree (FCWS-T). We separate the nodes in FCWS-T into two types, namely the control nodes and the service nodes, leading to the representation of various composition structures can be explicitly performed. Then, a reliability simulation method is proposed based on FCWS-T and it can effectively analyze the reliability of a complex Web service. Experiments on a financial management service show the effectiveness of our approach for fault-tolerant Web service compositions.

Feature Analysis for Duplicate Detection in Programming QA Communities

In community question answering (CQA), duplicate questions are questions that were previously created and answered but occur again. These questions produce noises in the CQA websites which impede users to find answers efficiently. Programming CQA (PCQA), a branch of CQA that holds questions related to programming, also suffers from this problem. Existing works on duplicate detection in PCQA websites framed the task as a supervised learning task on the question pairs, and relied on a number of extracted features of the question pairs. But they extracted only textual features and did not consider the source code in the questions, which are linguistically very different to natural languages. Our work focuses on developing novel features for PCQA duplicate detection. We leverage continuous word vectors from the deep learning literature, probabilistic models in information retrieval and association pairs mined from duplicate questions using machine translation. We provide extensive empirical analysis on the performance of these features and their various combinations using a range of learning models. Our work could be helpful for both research works and practical applications that require extracting features from texts that are not all natural languages.

A Hybrid QoS Evaluation Tool Based on the Cloud Computing Platform

Centralized and distributed evaluation approaches have been proposed for Quality of Services (QoS) measurement. The centralized evaluation approach cannot reflect the user-side QoS and the distributed evaluation approach depend on users to provide evaluation records. In this paper, a hybrid evaluation tool comprising two approaches is proposed. In particular, the centralized evaluation is deployed on a cloud computing platform which is the Amazon web services (AWS). Therefore, the hybrid tool can make evaluation from several AWS regions even if there are no test volunteers. Both the collaborative filtering model and the multiple regression model are implemented in the hybrid evaluation tool for predicting the unknown QoS value. To illustrate the advantages of the hybrid QoS evaluation tool, the scene of a traveler who wants to evaluate and select a best web service in the real world is presented. The results show that the hybrid tool is effective and convenient for users to evaluate the QoS of web services.