What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Algorithm
His scientific interests lie mostly in Parallel computing, CUDA, Speedup, Information retrieval and Database.
Parallel computing and Graphics are commonly linked in his work.
Qiong Luo interconnects General-purpose computing on graphics processing units, Central processing unit, High memory and Memory bandwidth in the investigation of issues within CUDA.
His studies deal with areas such as Tree, Search engine indexing and Index as well as Speedup.
His study in Information retrieval is interdisciplinary in nature, drawing from both Similarity, Data mining and Global Positioning System.
His Database study integrates concerns from other disciplines, such as Application server, Smart Cache, Cache, Database caching and Sargable.
His most cited work include:
- On supporting containment queries in relational database management systems (782 citations)
- Mars: a MapReduce framework on graphics processors (678 citations)
- RiMOM: A Dynamic Multistrategy Ontology Alignment Framework (362 citations)
What are the main themes of his work throughout his whole career to date?
Qiong Luo mainly focuses on Parallel computing, Data mining, Wireless sensor network, Database and Information retrieval.
His Parallel computing research integrates issues from Central processing unit and Graphics.
His research in Data mining intersects with topics in Hierarchical clustering, Cluster analysis and Routing.
His studies in Database integrate themes in fields like Proxy, The Internet and Cache.
Qiong Luo interconnects Web page and Peer-to-peer in the investigation of issues within Information retrieval.
His High memory research includes themes of Relational database and Memory bandwidth.
He most often published in these fields:
- Parallel computing (26.82%)
- Data mining (17.32%)
- Wireless sensor network (16.20%)
What were the highlights of his more recent work (between 2016-2021)?
- Parallel computing (26.82%)
- Graph (6.15%)
- Computation (6.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Parallel computing, Graph, Computation, Algorithm and Xeon Phi.
His study in the field of Parallel algorithm is also linked to topics like Translation lookaside buffer.
Qiong Luo has researched Parallel algorithm in several fields, including Tree, Backtracking and Speedup.
His Graph study which covers Theoretical computer science that intersects with Data structure and Construct.
The concepts of his Computation study are interwoven with issues in Hash table and Cluster analysis.
The study incorporates disciplines such as Bitmap, Vectorization and Central processing unit in addition to Xeon Phi.
Between 2016 and 2021, his most popular works were:
- Storytelling and Visualization: An Extended Survey (26 citations)
- Efficient Parallel Subgraph Enumeration on a Single Machine (11 citations)
- Accelerating pairwise SimRank estimation over static and dynamic graphs (10 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Algorithm
Qiong Luo spends much of his time researching Graph, Parallel computing, Vertex, Computation and Theoretical computer science.
His work in Xeon Phi and High memory are all subfields of Parallel computing research.
His Vertex study integrates concerns from other disciplines, such as Matching, Isomorphism and Data structure.
His Computation research is multidisciplinary, incorporating perspectives in Pruning, Cluster analysis and Enumeration.
His biological study spans a wide range of topics, including Sequential algorithm, Parallel algorithm, Scalability, Outlier and Bottleneck.
His Theoretical computer science research is multidisciplinary, relying on both Pairwise comparison, SimRank, Similarity matrix and Power graph analysis.
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