What is he best known for?
The fields of study he is best known for:
- Database
- Programming language
- Artificial intelligence
His scientific interests lie mostly in Data mining, Information retrieval, Theoretical computer science, XML and Database.
His Data mining research incorporates elements of Data stream, Set and Synthetic data.
His Query language and Query optimization study, which is part of a larger body of work in Information retrieval, is frequently linked to Popularity, bridging the gap between disciplines.
The various areas that he examines in his Theoretical computer science study include Algorithm, Computation, Complement and Benchmark.
His study on XML schema, XML validation and XML database is often connected to Twig as part of broader study in XML.
His Database research incorporates themes from String metric, String, String searching algorithm and Topology.
His most cited work include:
- Holistic twig joins: optimal XML pattern matching (905 citations)
- Structural joins: a primitive for efficient XML query pattern matching (802 citations)
- Answering Queries Using Views. (556 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Data mining, Information retrieval, Theoretical computer science, Database and XML.
Divesh Srivastava interconnects Data stream, Data quality, Tuple and Set in the investigation of issues within Data mining.
His study connects Data integration and Information retrieval.
His Theoretical computer science study frequently links to other fields, such as Algorithm.
In general XML, his work in XML validation, XML database and XPath is often linked to Twig linking many areas of study.
As a part of the same scientific study, Divesh Srivastava usually deals with the XML validation, concentrating on XML schema and frequently concerns with Efficient XML Interchange and Simple API for XML.
He most often published in these fields:
- Data mining (32.56%)
- Information retrieval (21.85%)
- Theoretical computer science (17.65%)
What were the highlights of his more recent work (between 2015-2021)?
- Data mining (32.56%)
- Differential privacy (4.20%)
- Algorithm (9.03%)
In recent papers he was focusing on the following fields of study:
Divesh Srivastava mainly investigates Data mining, Differential privacy, Algorithm, Theoretical computer science and Set.
The Data mining study combines topics in areas such as Data quality and Search engine indexing.
His work on Time complexity as part of general Algorithm research is frequently linked to Order, bridging the gap between disciplines.
His work on Range query as part of general Theoretical computer science study is frequently linked to Compatibility, bridging the gap between disciplines.
His Set research is multidisciplinary, relying on both Tuple, Functional dependency, Canonical form, Range and Data integrity.
He works mostly in the field of Data science, limiting it down to concerns involving Metadata and, occasionally, Information retrieval.
Between 2015 and 2021, his most popular works were:
- Online entity resolution using an Oracle (67 citations)
- Marginal Release Under Local Differential Privacy (55 citations)
- Privacy at Scale: Local Differential Privacy in Practice (52 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Database
Divesh Srivastava mainly focuses on Differential privacy, Data mining, Theoretical computer science, Information retrieval and Synthetic data.
His Differential privacy study combines topics from a wide range of disciplines, such as Function, Key, News aggregator and Range query.
His Data mining study combines topics in areas such as Pruning, Matching, Set, Structure and Event monitoring.
His Theoretical computer science study integrates concerns from other disciplines, such as Time complexity, Functional dependency, Canonical form, Task and Data integrity.
His Information retrieval research is multidisciplinary, incorporating elements of Process, Type and Index.
His Synthetic data research is multidisciplinary, incorporating perspectives in Data quality and Oracle.
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.
