H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 34 Citations 5,597 136 World Ranking 6493 National Ranking 310

Overview

What is he best known for?

The fields of study he is best known for:

  • Programming language
  • Database
  • Algorithm

His primary areas of study are Query optimization, Theoretical computer science, Dynamic programming, Path expression and Query language. His work carried out in the field of Query optimization brings together such families of science as Object Query Language, Relational database, Cardinality and Cardinality. His studies deal with areas such as Software, Semantics, Set and Materialized view as well as Theoretical computer science.

His Dynamic programming research includes themes of Cross product and Graph. His Path expression research includes elements of Object, Programming language and Structure. He has included themes like Data modeling, XML validation, Efficient XML Interchange, XML framework and Streaming XML in his Information retrieval study.

His most cited work include:

  • Querying Documents in Object Databases (247 citations)
  • Heuristic and randomized optimization for the join ordering problem (242 citations)
  • Efficient Storage of XML Data (214 citations)

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

The scientist’s investigation covers issues in Theoretical computer science, Query optimization, Programming language, XML and Query language. His studies in Theoretical computer science integrate themes in fields like Set, Heuristics, Algorithm and Sort-merge join, Joins. His Query optimization study is concerned with the larger field of Data mining.

The Programming language study which covers XQuery that intersects with Algebraic number. His research is interdisciplinary, bridging the disciplines of Database and XML. His study in Query language is interdisciplinary in nature, drawing from both Object Query Language, Algebraic expression and RDF query language.

He most often published in these fields:

  • Theoretical computer science (38.37%)
  • Query optimization (22.09%)
  • Programming language (17.44%)

What were the highlights of his more recent work (between 2005-2020)?

  • Theoretical computer science (38.37%)
  • Query optimization (22.09%)
  • Algorithm (13.37%)

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

Guido Moerkotte mainly focuses on Theoretical computer science, Query optimization, Algorithm, Programming language and Joins. The concepts of his Theoretical computer science study are interwoven with issues in Dynamic programming, Hash join, Sort-merge join, Recursive join and Heuristics. To a larger extent, Guido Moerkotte studies Data mining with the aim of understanding Query optimization.

His Algorithm research focuses on Query plan and how it connects with Factor and Value. As a member of one scientific family, he mostly works in the field of Programming language, focusing on XML and, on occasion, Information retrieval, Syntax and Message queue. His Joins research focuses on Pruning and how it relates to Enumeration and Compile time.

Between 2005 and 2020, his most popular works were:

  • Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins (155 citations)
  • Preventing bad plans by bounding the impact of cardinality estimation errors (82 citations)
  • Dynamic programming strikes back (67 citations)

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

  • Programming language
  • Algorithm
  • Database

Guido Moerkotte mainly investigates Algorithm, Theoretical computer science, Query optimization, Cardinality and Dynamic programming. His Algorithm study combines topics in areas such as Enumeration algorithm, Enumeration and Pruning. His Theoretical computer science research integrates issues from Operator, Group, Correctness, Tree and Speedup.

His Query optimization study necessitates a more in-depth grasp of Information retrieval. His Cardinality study combines topics from a wide range of disciplines, such as Cardinality, Data mining and Sargable. His work deals with themes such as Hypergraph, Graph, Cross product and Graph, which intersect with Dynamic programming.

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.

Top Publications

Heuristic and randomized optimization for the join ordering problem

Michael Steinbrunn;Guido Moerkotte;Alfons Kemper.
very large data bases (1997)

416 Citations

Efficient Storage of XML Data

C. Kanne;G. Moerkotte.
international conference on data engineering (2000)

382 Citations

Anatomy of a native XML base management system

T. Fiebig;S. Helmer;C.-C. Kanne;G. Moerkotte.
very large data bases (2002)

298 Citations

Object-oriented database management: applications in engineering and computer science

Alfons Kemper;Guido Moerkotte.
(1994)

252 Citations

Querying Documents in Object Databases

Serge Abiteboul;Sophie Cluet;Vassilis Christophides;Tova Milo.
International Journal on Digital Libraries (1997)

248 Citations

The implementation and performance of compressed databases

Till Westmann;Donald Kossmann;Sven Helmer;Guido Moerkotte.
international conference on management of data (2000)

242 Citations

Characteristic sets: Accurate cardinality estimation for RDF queries with multiple joins

Thomas Neumann;Guido Moerkotte.
international conference on data engineering (2011)

236 Citations

Access support in object bases

Alfons Kemper;Guido Moerkotte.
international conference on management of data (1990)

233 Citations

Advanced Query Processing in Object Bases Using Access Support Relations

Alfons Kemper;Guido Moerkotte.
very large data bases (1990)

172 Citations

Nested Queries in Object Bases

Sophie Cluet;Guido Moerkotte.
database programming languages (1993)

160 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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