What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Database
Distributed computing, Stream processing, Parallel computing, Scalability and Scheduling are his primary areas of study.
His biological study spans a wide range of topics, including Parallel processing, Serializability, Non-lock concurrency control, Optimistic concurrency control and Multiversion concurrency control.
His Stream processing study combines topics in areas such as Middleware, Real-time computing and Operator.
His Parallel computing study integrates concerns from other disciplines, such as Controller and Disk array.
His Scheduling study incorporates themes from Software, FLEX and Implementation.
His work focuses on many connections between Data mining and other disciplines, such as Node, that overlap with his field of interest in Search engine indexing.
His most cited work include:
- System and method of multiparty billing for web access (490 citations)
- SPADE: the system s declarative stream processing engine (462 citations)
- Horting hatches an egg: a new graph-theoretic approach to collaborative filtering (367 citations)
What are the main themes of his work throughout his whole career to date?
Kun-Lung Wu spends much of his time researching Distributed computing, Stream processing, Parallel computing, Data mining and Real-time computing.
His study looks at the intersection of Distributed computing and topics like Computer network with Distributed algorithm.
Kun-Lung Wu usually deals with Stream processing and limits it to topics linked to Scalability and Code generation.
His work on Thrashing as part of general Parallel computing research is often related to Tuple, thus linking different fields of science.
The concepts of his Data mining study are interwoven with issues in Data stream and Search engine indexing.
His Search engine indexing research includes themes of Range query, Set and Index.
He most often published in these fields:
- Distributed computing (27.66%)
- Stream processing (25.11%)
- Parallel computing (17.87%)
What were the highlights of his more recent work (between 2010-2020)?
- Stream processing (25.11%)
- Distributed computing (27.66%)
- Parallel computing (17.87%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Stream processing, Distributed computing, Parallel computing, Theoretical computer science and Tuple.
His research integrates issues of Scalability, Graph, Resource allocation, Data stream mining and Stream in his study of Stream processing.
His work in the fields of Fault tolerance overlaps with other areas such as Stateful firewall.
His study focuses on the intersection of Parallel computing and fields such as Automatic parallelization with connections in the field of Throughput.
His work carried out in the field of Theoretical computer science brings together such families of science as Hypercube graph, Leverage, Cluster analysis and Graph.
His Scheduling research focuses on Piggybacking and how it connects with Real-time computing.
Between 2010 and 2020, his most popular works were:
- Elastic Scaling for Data Stream Processing (170 citations)
- Counting and sampling triangles from a graph stream (145 citations)
- Streaming algorithms for k-core decomposition (99 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Database
His primary scientific interests are in Stream processing, Distributed computing, Parallel computing, Theoretical computer science and Graph.
Kun-Lung Wu conducted interdisciplinary study in his works that combined Stream processing and Tuple.
His study in Distributed computing is interdisciplinary in nature, drawing from both Scalability, Automatic parallelization, Piggybacking, Middleware and Scheduling.
He combines subjects such as Bottleneck and Middleware with his study of Parallel computing.
The Theoretical computer science study combines topics in areas such as Complement graph and Graph bandwidth.
His research in Graph tackles topics such as Connected component which are related to areas like Modular decomposition, Clique, Cycle graph, Induced subgraph isomorphism problem and Vertex connectivity.
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