What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Computer network
Distributed computing, Database, Data mining, Operating system and Parallel computing are his primary areas of study.
Kenneth C. Sevcik has included themes like Layered queueing network, Queueing network models, G-network and Distributed concurrency control, Concurrency control in his Distributed computing study.
His work on Query optimization as part of general Database research is frequently linked to Quality, bridging the gap between disciplines.
His Data mining research includes elements of Information bottleneck method, Mutual information, Cluster analysis and Bottleneck.
Many of his research projects under Operating system are closely connected to Software design with Software design, tying the diverse disciplines of science together.
His work in the fields of Computer multitasking overlaps with other areas such as Scalable parallelism, Dependency graph, Single parameter and Data parallelism.
His most cited work include:
- The Grid File: An Adaptable, Symmetric Multikey File Structure (1092 citations)
- Quantitative system performance: computer system analysis using queueing network models (1026 citations)
- Theory and Practice in Parallel Job Scheduling (442 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Distributed computing, Parallel computing, Queueing theory, Scheduling and Data mining.
His Distributed computing research includes themes of Variable bitrate, Multiprocessor scheduling, Concurrency control and Server.
His Parallel computing study combines topics from a wide range of disciplines, such as Earliest deadline first scheduling and Round-robin scheduling.
In the subject of general Queueing theory, his work in Mean value analysis and Layered queueing network is often linked to Distribution, thereby combining diverse domains of study.
His research in Scheduling focuses on subjects like Speedup, which are connected to Workload.
His work in the fields of Data mining, such as Query optimization, intersects with other areas such as Spatial analysis.
He most often published in these fields:
- Distributed computing (30.23%)
- Parallel computing (20.93%)
- Queueing theory (16.28%)
What were the highlights of his more recent work (between 2000-2008)?
- Variable bitrate (8.14%)
- Data mining (12.79%)
- Algorithm (11.63%)
In recent papers he was focusing on the following fields of study:
Kenneth C. Sevcik mainly focuses on Variable bitrate, Data mining, Algorithm, Server and Scalability.
His work deals with themes such as Distributed computing, Data striping and STREAMS, which intersect with Variable bitrate.
Kenneth C. Sevcik has researched STREAMS in several fields, including Instruction prefetch and Parallel computing.
His work on Query optimization as part of his general Data mining study is frequently connected to Histogram matching, thereby bridging the divide between different branches of science.
He interconnects Convergence, Queueing theory and Mean value analysis in the investigation of issues within Algorithm.
His Server research incorporates elements of Scheduling and Bandwidth.
Between 2000 and 2008, his most popular works were:
- LIMBO: Scalable Clustering of Categorical Data (211 citations)
- Server-based smoothing of variable bit-rate streams (25 citations)
- A multi-dimensional histogram for selectivity estimation and fast approximate query answering (17 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Computer network
Kenneth C. Sevcik mostly deals with Query optimization, Set, Data mining, Online analytical processing and Data visualization.
The study incorporates disciplines such as Cardinality, Column, Data set and Relational database in addition to Query optimization.
The various areas that Kenneth C. Sevcik examines in his Data mining study include Data modeling, Scalability, Mutual information and Cluster analysis.
His Online analytical processing research overlaps with other disciplines such as View, IBM, Query by Example, Algorithm and Information extraction.
His Data visualization studies intersect with other subjects such as Hierarchical database model, Overhead and Minimum description length.
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