What is he best known for?
The fields of study he is best known for:
- Database
- Programming language
- Artificial intelligence
Surajit Chaudhuri focuses on Data mining, Information retrieval, Query optimization, Database and Query language.
The various areas that Surajit Chaudhuri examines in his Data mining study include Tuple, Workload, Sampling, Histogram and Sample.
His Information retrieval research includes elements of Ranking and Domain.
His Query optimization study incorporates themes from Query plan, Sargable, Query by Example, Query expansion and Statistics.
While the research belongs to areas of Database, Surajit Chaudhuri spends his time largely on the problem of Set, intersecting his research to questions surrounding Task, Selection and Online analytical processing.
His work carried out in the field of Query language brings together such families of science as Skyline, Web query classification and Relational database management system.
His most cited work include:
- An overview of data warehousing and OLAP technology (2349 citations)
- DBXplorer: a system for keyword-based search over relational databases (731 citations)
- An overview of business intelligence technology (601 citations)
What are the main themes of his work throughout his whole career to date?
Surajit Chaudhuri mainly investigates Data mining, Database, Information retrieval, Query optimization and Relational database.
The study incorporates disciplines such as Tuple, Workload, Set, Sampling and Sample in addition to Data mining.
His Database study focuses mostly on SQL, Database design, Database server, View and Data warehouse.
His Information retrieval research focuses on World Wide Web and how it relates to Cloud computing.
His biological study spans a wide range of topics, including Query by Example, Online aggregation, Sargable, Query language and Query expansion.
His Query expansion research integrates issues from Spatial query and Web query classification.
He most often published in these fields:
- Data mining (33.57%)
- Database (28.10%)
- Information retrieval (26.43%)
What were the highlights of his more recent work (between 2013-2021)?
- Information retrieval (26.43%)
- Set (12.14%)
- Data mining (33.57%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Information retrieval, Set, Data mining, Query optimization and Database.
His Information retrieval study combines topics in areas such as Tuple, Data set, Table and Knowledge base.
Surajit Chaudhuri has included themes like Pruning, Index, Confidence interval, Sampling and Algorithm in his Set study.
His research integrates issues of Probabilistic logic and Search engine indexing in his study of Data mining.
The Query optimization study combines topics in areas such as Theoretical computer science, Query by Example, Reduction, Benchmark and Big data.
His Query by Example study integrates concerns from other disciplines, such as Query language, Query expansion and Sargable.
Between 2013 and 2021, his most popular works were:
- Overview of Data Exploration Techniques (158 citations)
- Quickr: Lazily Approximating Complex AdHoc Queries in BigData Clusters (84 citations)
- Approximate Query Processing: No Silver Bullet (78 citations)
In his most recent research, the most cited papers focused on:
- Database
- Programming language
- Artificial intelligence
His main research concerns Data mining, Query optimization, Information retrieval, Database and Set.
The various areas that Surajit Chaudhuri examines in his Data mining study include Sample and Search engine indexing.
His Query optimization research is multidisciplinary, incorporating perspectives in Theoretical computer science, Query by Example, Predicate, Machine learning and Big data.
His biological study spans a wide range of topics, including Query language, Query expansion and Sargable.
His studies deal with areas such as Structure, Tuple, Knowledge base and Join as well as Information retrieval.
His work on Relational database and Web tables as part of general Database study is frequently linked to Data as a service, bridging the gap between disciplines.
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