Q
Qiang He
Researcher at Swinburne University of Technology
Publications - 547
Citations - 13588
Qiang He is an academic researcher from Swinburne University of Technology. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 50, co-authored 389 publications receiving 8498 citations. Previous affiliations of Qiang He include Huazhong University of Science and Technology & Shanghai Jiao Tong University.
Papers
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Journal ArticleDOI
Data-Driven Web APIs Recommendation for Building Web Applications
TL;DR: WAR (Web APIs Recommendation), the first data-driven approach for web APIs recommendation that integrates web API discovery, verification and selection operations based on keywords search over the web API correlation graph, is proposed.
Journal ArticleDOI
Response of Desulfovibrio vulgaris to Alkaline Stress
Sergey Stolyar,Qiang He,Marcin P. Joachimiak,Zhili He,Zamin Koo Yang,Sharon Borglin,Dominique C. Joyner,Katherine H. Huang,Eric J. Alm,Terry C. Hazen,Jizhong Zhou,Jizhong Zhou,Judy D. Wall,Adam P. Arkin,Adam P. Arkin,David A. Stahl +15 more
TL;DR: Genes previously reported to be up-regulated in E. coli were up-regulation in D. vulgaris and genes involved in flagellum synthesis were down-regulated; the transcriptional data also identified regulators, distinct from sigma S and sigma E, that are likely part of a D. Vulgaris Hildenborough-specific stress response system.
Book
The Design of Cloud Workflow Systems
TL;DR: This book presents a generic framework to facilitate a unified design and development process for software components that deliver lifecycle support for different QoS requirements and provides comprehensive introductions to the design of the system architecture, system functionality and QoS management.
Proceedings ArticleDOI
LSHiForest: A Generic Framework for Fast Tree Isolation Based Ensemble Anomaly Analysis
Xuyun Zhang,Wanchun Dou,Qiang He,Rui Zhou,Christopher Leckie,Ramamohanarao Kotagiri,Zoran Salcic +6 more
TL;DR: A generic framework for fast tree isolation based ensemble anomaly analysis with the use of a Locality-Sensitive Hashing (LSH) forest, which can be instantiated with a diverse range of LSH families, and the fast isolation mechanism can be extended to any distance measures, data types and data spaces where an LSH family is defined.