What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- The Internet
Divyakant Agrawal mainly focuses on Database, Scalability, Distributed computing, Data mining and Cloud computing.
His Database study focuses on Database transaction in particular.
His Scalability research is multidisciplinary, relying on both Data management, Reliability, Cache and The Internet, World Wide Web.
His Distributed computing research incorporates elements of Tree, Wireless sensor network and Protocol.
The Data mining study combines topics in areas such as Data set and k-nearest neighbors algorithm.
His Cloud computing research includes elements of Web application and Data science.
His most cited work include:
- Limiting the spread of misinformation in social networks (512 citations)
- Medians and beyond: new aggregation techniques for sensor networks (475 citations)
- Efficient computation of frequent and top-k elements in data streams (473 citations)
What are the main themes of his work throughout his whole career to date?
Divyakant Agrawal spends much of his time researching Distributed computing, Database, Scalability, Data mining and Cloud computing.
His Distributed computing study incorporates themes from Serializability, Protocol and Concurrency control.
The concepts of his Database study are interwoven with issues in World Wide Web and Cache.
The various areas that Divyakant Agrawal examines in his Scalability study include Data management, Computer security, The Internet and Computer network, Server.
He has included themes like Theoretical computer science and Information retrieval in his Data mining study.
His work in Data warehouse addresses issues such as Data cube, which are connected to fields such as Online analytical processing.
He most often published in these fields:
- Distributed computing (31.39%)
- Database (22.87%)
- Scalability (19.13%)
What were the highlights of his more recent work (between 2012-2021)?
- Distributed computing (31.39%)
- Scalability (19.13%)
- Cloud computing (8.73%)
In recent papers he was focusing on the following fields of study:
Divyakant Agrawal mostly deals with Distributed computing, Scalability, Cloud computing, Database transaction and Database.
His studies deal with areas such as Distributed transaction, Data management, Concurrency control and Online transaction processing as well as Distributed computing.
His Scalability study combines topics from a wide range of disciplines, such as Replication, Computer security, Cryptography, Online analytical processing and Server.
His biological study spans a wide range of topics, including Set and Protocol.
As a member of one scientific family, Divyakant Agrawal mostly works in the field of Database transaction, focusing on Blockchain and, on occasion, Confidentiality and Cryptocurrency.
His work on Data structure expands to the thematically related Database.
Between 2012 and 2021, his most popular works were:
- ElasTraS: An elastic, scalable, and self-managing transactional database for the cloud (142 citations)
- Preserving Location Privacy in Geosocial Applications (92 citations)
- $\mathcal{MD}$-HBase: design and implementation of an elastic data infrastructure for cloud-scale location services (70 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- The Internet
Divyakant Agrawal focuses on Database transaction, Scalability, Database, Distributed computing and Transaction processing.
His research in Database transaction intersects with topics in Commit and Blockchain.
His Scalability study combines topics in areas such as Online analytical processing, Data mining, Data cube and Relational database management system.
His research ties Cloud computing and Database together.
His Distributed computing research is multidisciplinary, incorporating elements of Confidentiality and Online transaction processing.
His work on Distributed transaction as part of general Transaction processing study is frequently connected to Logging, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
