What is he best known for?
The fields of study he is best known for:
- Operating system
- The Internet
- Artificial intelligence
Carl Kesselman mostly deals with Distributed computing, Grid computing, World Wide Web, Grid Security Infrastructure and Service.
His study on Metacomputing is often connected to Replica as part of broader study in Distributed computing.
His Grid computing research incorporates themes from The Internet, Semantic grid and Data science.
His Data science research integrates issues from Storage Resource Broker and Interoperability.
The various areas that Carl Kesselman examines in his Open Grid Services Architecture study include Web service, Instrumentation and Commodity computing.
The study incorporates disciplines such as Grid application and Utility computing in addition to DRMAA.
His most cited work include:
- The Grid 2: Blueprint for a New Computing Infrastructure (7258 citations)
- The Anatomy of the Grid: Enabling Scalable Virtual Organizations (6490 citations)
- The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration (3083 citations)
What are the main themes of his work throughout his whole career to date?
Carl Kesselman mainly focuses on Distributed computing, Grid computing, Data science, World Wide Web and Workflow.
The Distributed computing study which covers Data grid that intersects with GridFTP.
His Grid computing study deals with Semantic grid intersecting with Shared resource.
His work carried out in the field of Data science brings together such families of science as Terabyte, Data management, Interoperability and Big data.
His work in World Wide Web addresses issues such as Service, which are connected to fields such as Context.
His biological study spans a wide range of topics, including Executable and Computer graphics.
He most often published in these fields:
- Distributed computing (22.26%)
- Grid computing (23.67%)
- Data science (20.49%)
What were the highlights of his more recent work (between 2012-2021)?
- Data science (20.49%)
- Big data (3.53%)
- Metadata (6.36%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Data science, Big data, Metadata, Data management and World Wide Web.
Carl Kesselman combines subjects such as Grid computing, Data-driven and Translational medicine with his study of Data science.
His study in Big data is interdisciplinary in nature, drawing from both Resource, Software, Analytics and Interoperability.
His studies in Metadata integrate themes in fields like Cell culture, Service, Relational database, Relational database management system and Workflow.
He has researched Service in several fields, including Entity–relationship model and Metadata management.
His World Wide Web research is multidisciplinary, relying on both Context and Data warehouse.
Between 2012 and 2021, his most popular works were:
- A physical sciences network characterization of non-tumorigenic and metastatic cells (115 citations)
- Conserved and Divergent Features of Human and Mouse Kidney Organogenesis (93 citations)
- Predictive Big Data Analytics: A Study of Parkinson’s Disease Using Large, Complex, Heterogeneous, Incongruent, Multi-Source and Incomplete Observations (63 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- The Internet
His main research concerns Data science, Big data, Metadata, Cell culture and Cell biology.
Carl Kesselman interconnects Disparate system, Resource and Workflow in the investigation of issues within Data science.
His Resource study which covers Faceted search that intersects with Bioinformatics.
While the research belongs to areas of Big data, he spends his time largely on the problem of Data management, intersecting his research to questions surrounding Terabyte, Usability, Interoperability, Cloud computing and Machine learning.
The subject of his Metadata research is within the realm of World Wide Web.
His Cell culture research incorporates elements of Kidney and Neuroscience.
This overview was generated by a machine learning system which analysed the scientist’s
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