H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 89 Citations 32,300 309 World Ranking 281 National Ranking 171

Research.com Recognitions

Awards & Achievements

2017 - Fellow of the American Association for the Advancement of Science (AAAS)

2003 - ACM Fellow For contributions to database technology.

Overview

What is he best known for?

The fields of study he is best known for:

  • Database
  • Operating system
  • Programming language

His primary scientific interests are in Data mining, XML, Database, Query language and Query optimization. His work carried out in the field of Data mining brings together such families of science as Tree, Theoretical computer science and Set. XML database, XML validation and XML schema are the core of his XML study.

His study looks at the intersection of Database and topics like Edit distance with Wagner–Fischer algorithm. His work investigates the relationship between Query language and topics such as RDF query language that intersect with problems in Query by Example. His Query optimization course of study focuses on Query expansion and Web query classification and View.

His most cited work include:

  • Structural joins: a primitive for efficient XML query pattern matching (802 citations)
  • Challenges and opportunities with big data (651 citations)
  • Efficient retrieval of similar time sequences under time warping (606 citations)

What are the main themes of his work throughout his whole career to date?

Hosagrahar V. Jagadish mainly investigates Database, Information retrieval, Data mining, Theoretical computer science and XML. Hosagrahar V. Jagadish works in the field of Data mining, namely Relational database. Many of his studies involve connections with topics such as Programming language and XML.

His research in XML database tackles topics such as XML validation which are related to areas like Efficient XML Interchange and XML Schema Editor. His studies in XML schema integrate themes in fields like Simple API for XML, XML Signature and XML framework. His Query language study integrates concerns from other disciplines, such as Web query classification and RDF query language.

He most often published in these fields:

  • Database (18.20%)
  • Information retrieval (16.99%)
  • Data mining (15.29%)

What were the highlights of his more recent work (between 2017-2021)?

  • Information retrieval (16.99%)
  • Artificial intelligence (9.95%)
  • Set (8.74%)

In recent papers he was focusing on the following fields of study:

His scientific interests lie mostly in Information retrieval, Artificial intelligence, Set, Data science and Ranking. His Information retrieval research includes elements of Data model, Metadata and Natural language. His work deals with themes such as Relational database, Inference and Joins, which intersect with Natural language.

His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Pattern recognition and Natural language processing. His Set study typically links adjacent topics like Theoretical computer science. His SQL study is focused on Database in general.

Between 2017 and 2021, his most popular works were:

  • SLADE: A Smart Large-Scale Task Decomposer in Crowdsourcing (59 citations)
  • Designing Fair Ranking Schemes (45 citations)
  • Online set selection with fairness and diversity constraints (41 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Database
  • Programming language

Hosagrahar V. Jagadish spends much of his time researching Ranking, Set, Theoretical computer science, Data science and Task. His Theoretical computer science research integrates issues from Approximation algorithm, Steiner tree problem, Query language, Relational database and Knowledge-based systems. The Data science study combines topics in areas such as Value, Transparency, Categorical variable and Pattern matching.

His Learning to rank research is within the category of Information retrieval. His Information retrieval research focuses on Natural language and how it relates to Database schema and SQL. His work in Artificial intelligence tackles topics such as Correctness which are related to areas like Crowdsourcing.

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.

Best Publications

Structural joins: a primitive for efficient XML query pattern matching

S. Al-Khalifa;H.V. Jagadish;N. Koudas;J.M. Patel.
international conference on data engineering (2002)

1285 Citations

Challenges and opportunities with big data

Alexandros Labrinidis;H. V. Jagadish.
very large data bases (2012)

930 Citations

Big data and its technical challenges

H. V. Jagadish;Johannes Gehrke;Alexandros Labrinidis;Yannis Papakonstantinou.
Communications of The ACM (2014)

919 Citations

Efficient retrieval of similar time sequences under time warping

Byoung-Kee Yi;H.V. Jagadish;C. Faloutsos.
international conference on data engineering (1998)

904 Citations

Analysis of the clustering properties of the Hilbert space-filling curve

B. Moon;H.V. Jagadish;C. Faloutsos;J.H. Saltz.
IEEE Transactions on Knowledge and Data Engineering (2001)

866 Citations

The TV-tree: an index structure for high-dimensional data

King Ip Lin;H. V. Jagadish;Christos Faloutsos.
very large data bases (1994)

859 Citations

Approximate String Joins in a Database (Almost) for Free

Luis Gravano;Panagiotis G. Ipeirotis;H. V. Jagadish;Nick Koudas.
very large data bases (2001)

750 Citations

iDistance: An adaptive B + -tree based indexing method for nearest neighbor search

H. V. Jagadish;Beng Chin Ooi;Kian-Lee Tan;Cui Yu.
ACM Transactions on Database Systems (2005)

687 Citations

Linear clustering of objects with multiple attributes

H. V. Jagadish.
international conference on management of data (1990)

655 Citations

TIMBER: A native XML database

H. V. Jagadish;S. Al-Khalifa;A. Chapman;L. V. S. Lakshmanan.
very large data bases (2002)

618 Citations

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