H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 64 Citations 17,175 160 World Ranking 1240 National Ranking 49

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • The Internet

Data mining, Information retrieval, Database, XML and Query optimization are his primary areas of study. His Data mining study focuses on Query language in particular. In his study, Function is strongly linked to Ranking, which falls under the umbrella field of Information retrieval.

Nick Koudas usually deals with Database and limits it to topics linked to String metric and Edit distance, Commentz-Walter algorithm and Approximate string matching. His work on XML database, XML validation and XML schema is typically connected to Twig as part of general XML study, connecting several disciplines of science. Nick Koudas interconnects View and Approximation algorithm in the investigation of issues within Query optimization.

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)
  • TwitterMonitor: trend detection over the twitter stream (742 citations)

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

His scientific interests lie mostly in Data mining, Information retrieval, Theoretical computer science, Set and Algorithm. Nick Koudas has researched Data mining in several fields, including Data set, Search engine indexing and Artificial intelligence. His research in Information retrieval intersects with topics in Ranking, XML and Database, Identification.

His Theoretical computer science study incorporates themes from Matching, Joins, Approximate string matching and Substring. In his research, Skyline and Categorical variable is intimately related to Tuple, which falls under the overarching field of Set. His research in the fields of Time complexity overlaps with other disciplines such as Similarity.

He most often published in these fields:

  • Data mining (48.35%)
  • Information retrieval (21.98%)
  • Theoretical computer science (15.38%)

What were the highlights of his more recent work (between 2014-2021)?

  • Data mining (48.35%)
  • Artificial intelligence (11.54%)
  • Deep learning (4.95%)

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

His primary areas of investigation include Data mining, Artificial intelligence, Deep learning, Set and Frame. His study in the fields of Relational database under the domain of Data mining overlaps with other disciplines such as Data collection. Nick Koudas studied Artificial intelligence and Machine learning that intersect with Aggregate.

His Deep learning research is multidisciplinary, incorporating perspectives in Algorithm, Range query, Overhead and String. In his research on the topic of Set, Extensibility, Identification, Graph and Distributed computing is strongly related with Process. His studies in Object integrate themes in fields like Window, Tracking and Information retrieval.

Between 2014 and 2021, his most popular works were:

  • Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries (11 citations)
  • SVQ: Streaming Video Queries (11 citations)
  • Multi-Attribute Selectivity Estimation Using Deep Learning. (11 citations)

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

  • Artificial intelligence
  • Database
  • The Internet

His primary areas of study are Deep learning, Artificial intelligence, Data mining, Set and Speedup. His work carried out in the field of Deep learning brings together such families of science as Range query, Query optimization and Natural language processing. His study on Visualization is often connected to Matching, Space and Task as part of broader study in Artificial intelligence.

Nick Koudas mostly deals with Relational database in his studies of Data mining. The various areas that he examines in his Set study include Subspace topology, Tuple, Perspective and Categorical variable. His Speedup research includes elements of Object detection, Real-time computing and Video processing.

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

Holistic twig joins: optimal XML pattern matching

Nicolas Bruno;Nick Koudas;Divesh Srivastava.
international conference on management of data (2002)

1426 Citations

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

TwitterMonitor: trend detection over the twitter stream

Michael Mathioudakis;Nick Koudas.
international conference on management of data (2010)

1133 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

Optimal Histograms with Quality Guarantees

H. V. Jagadish;Nick Koudas;S. Muthukrishnan;Viswanath Poosala.
very large data bases (1998)

555 Citations

Monitoring k-nearest neighbor queries over moving objects

X. Yu;K.Q. Pu;N. Koudas.
international conference on data engineering (2005)

465 Citations

Aggregate Query Answering on Anonymized Tables

Qing Zhang;N. Koudas;D. Srivastava;Ting Yu.
international conference on data engineering (2007)

449 Citations

PREFER: a system for the efficient execution of multi-parametric ranked queries

Vagelis Hristidis;Nick Koudas;Yannis Papakonstantinou.
international conference on management of data (2001)

401 Citations

Data-streams and histograms

Sudipto Guha;Nick Koudas;Kyuseok Shim.
symposium on the theory of computing (2001)

398 Citations

Record linkage: similarity measures and algorithms

Nick Koudas;Sunita Sarawagi;Divesh Srivastava.
international conference on management of data (2006)

369 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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