What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
His primary scientific interests are in Distributed computing, Scheduling, Fair-share scheduling, Real-time computing and Dynamic priority scheduling.
His biological study spans a wide range of topics, including Schedule, Resource allocation, Embedded system and Computer security model.
His Resource allocation research focuses on Cloud computing and how it connects with Online optimization.
Xiao Qin has included themes like Parallel algorithm, Parallel computing and Backup in his Scheduling study.
The concepts of his Parallel computing study are interwoven with issues in Energy efficient scheduling, Algorithm design and Cluster analysis.
His Earliest deadline first scheduling research incorporates themes from Round-robin scheduling, Least slack time scheduling and Fixed-priority pre-emptive scheduling.
His most cited work include:
- Improving MapReduce performance through data placement in heterogeneous Hadoop clusters (339 citations)
- Online optimization for scheduling preemptable tasks on IaaS cloud systems (302 citations)
- Scheduling security-critical real-time applications on clusters (117 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Distributed computing, Scheduling, Parallel computing, Real-time computing and Workload.
His work deals with themes such as Resource allocation, Dynamic priority scheduling and Fair-share scheduling, which intersect with Distributed computing.
His Scheduling study frequently links to related topics such as Schedule.
His studies in Parallel computing integrate themes in fields like Input/output and Computer cluster.
His Real-time computing study integrates concerns from other disciplines, such as Data center, Software security assurance, Computer security model and Computer data storage.
His research integrates issues of Dynamic load balancing and Server in his study of Workload.
He most often published in these fields:
- Distributed computing (35.53%)
- Scheduling (21.71%)
- Parallel computing (16.45%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (8.55%)
- Data mining (7.24%)
- Scalability (5.59%)
In recent papers he was focusing on the following fields of study:
Xiao Qin spends much of his time researching Artificial intelligence, Data mining, Scalability, Distributed computing and Cluster analysis.
The study incorporates disciplines such as Machine learning, Pattern recognition, Computer vision and Natural language processing in addition to Artificial intelligence.
His research investigates the connection between Data mining and topics such as Outlier that intersect with issues in Anomaly detection.
His study looks at the relationship between Scalability and fields such as Data center, as well as how they intersect with chemical problems.
The various areas that he examines in his Distributed computing study include Workload, Scheduling, Server and Erasure code.
Key is closely connected to Tree in his research, which is encompassed under the umbrella topic of Scheduling.
Between 2018 and 2021, his most popular works were:
- Detecting fake news for reducing misinformation risks using analytics approaches (24 citations)
- Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding. (14 citations)
- Scalable Kernel Density Estimation-based Local Outlier Detection over Large Data Streams. (11 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
Xiao Qin mainly investigates Data mining, Linear subspace, Cluster analysis, Artificial intelligence and Anomaly detection.
His research on Cluster analysis frequently connects to adjacent areas such as Categorical variable.
His Categorical variable research incorporates elements of Hierarchical clustering, Entropy, Feature extraction, Pattern recognition and Speedup.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Named-entity recognition.
His Machine learning research includes elements of Smoothing, Algorithm design and Microbiome.
His Anomaly detection research is multidisciplinary, incorporating elements of Interpretability, Data stream mining, Scalability and Outlier.
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