H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 51 Citations 9,629 133 World Ranking 2742 National Ranking 107

Research.com Recognitions

Awards & Achievements

2020 - ACM Fellow For contributions to data cleaning and data integration

2014 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • Programming language

His main research concerns Data mining, Data quality, Query optimization, Data integrity and Theoretical computer science. In his research, Ihab F. Ilyas performs multidisciplinary study on Data mining and Tuple. Ihab F. Ilyas has researched Query optimization in several fields, including Relational database and Sargable.

His Sargable research integrates issues from Query language, Query expansion and Ranking. His Data integrity research is multidisciplinary, incorporating elements of Domain, Scalability, Missing data, Data science and Data set. His studies in Theoretical computer science integrate themes in fields like Functional dependency, Pipeline, Semantics, Rank and Ranking.

His most cited work include:

  • A survey of top-k query processing techniques in relational database systems (734 citations)
  • Top-k Query Processing in Uncertain Databases (397 citations)
  • Supporting top- k join queries in relational databases (370 citations)

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

Information retrieval, Data mining, Artificial intelligence, Database and Theoretical computer science are his primary areas of study. His work is dedicated to discovering how Information retrieval, Data structure are connected with Graph and other disciplines. In the subject of general Data mining, his work in Data integration is often linked to Data quality, thereby combining diverse domains of study.

His work deals with themes such as Machine learning and Natural language processing, which intersect with Artificial intelligence. He interconnects Functional dependency and Rank in the investigation of issues within Theoretical computer science. His Query optimization study combines topics in areas such as Query language, Relational database, Query expansion and Web search query, Web query classification.

He most often published in these fields:

  • Information retrieval (30.81%)
  • Data mining (27.91%)
  • Artificial intelligence (16.28%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (16.28%)
  • Theoretical computer science (15.70%)
  • Training set (4.65%)

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

Ihab F. Ilyas mainly investigates Artificial intelligence, Theoretical computer science, Training set, Data integrity and Set. His Artificial intelligence research includes elements of Machine learning and Pattern recognition. His Theoretical computer science research focuses on subjects like Functional dependency, which are linked to Computation and Distributed computing.

His Data integrity research incorporates elements of Reliability and Knowledge representation and reasoning. The various areas that he examines in his Set study include Sampling, Data deduplication and Data mining. His Data mining research is multidisciplinary, relying on both Search algorithm and Rule-based system.

Between 2018 and 2021, his most popular works were:

  • Data Cleaning (34 citations)
  • HoloDetect: Few-Shot Learning for Error Detection (27 citations)
  • A Formal Framework for Probabilistic Unclean Databases. (21 citations)

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

  • Artificial intelligence
  • Database
  • Programming language

His primary areas of investigation include Artificial intelligence, Data integrity, Tuple, Space and Database. His research on Artificial intelligence often connects related areas such as Machine learning. The Data integrity study combines topics in areas such as Class, Functional dependency and Theoretical computer science.

Other disciplines of study, such as Computational problem, Cardinality, Noise, Realization and Probabilistic database, are mixed together with his Tuple studies. Along with Space, other disciplines of study including Distributed algorithm, Dependency, Pruning, Data processing and Algorithm are integrated into his research. His Database research is multidisciplinary, incorporating perspectives in Probabilistic logic and Noisy channel model.

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

A survey of top-k query processing techniques in relational database systems

Ihab F. Ilyas;George Beskales;Mohamed A. Soliman.
ACM Computing Surveys (2008)

1006 Citations

Top-k Query Processing in Uncertain Databases

M. A. Soliman;I. F. Ilyas;K. Chen-Chuan Chang.
international conference on data engineering (2007)

578 Citations

Supporting top- k join queries in relational databases

Ihab F. Ilyas;Walid G. Aref;Ahmed K. Elmagarmid.
very large data bases (2004)

562 Citations

CORDS: automatic discovery of correlations and soft functional dependencies

Ihab F. Ilyas;Volker Markl;Peter Haas;Paul Brown.
international conference on management of data (2004)

363 Citations

RankSQL: query algebra and optimization for relational top-k queries

Chengkai Li;Kevin Chen-Chuan Chang;Ihab F. Ilyas;Sumin Song.
international conference on management of data (2005)

349 Citations

NADEEF: a commodity data cleaning system

Michele Dallachiesa;Amr Ebaid;Ahmed Eldawy;Ahmed Elmagarmid.
international conference on management of data (2013)

309 Citations

Holistic data cleaning: Putting violations into context

Xu Chu;I. F. Ilyas;P. Papotti.
international conference on data engineering (2013)

292 Citations

KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing

Xu Chu;John Morcos;Ihab F. Ilyas;Mourad Ouzzani.
international conference on management of data (2015)

248 Citations

Data Curation at Scale: The Data Tamer System

Michael Stonebraker;Daniel Bruckner;Ihab F. Ilyas;George Beskales.
conference on innovative data systems research (2013)

217 Citations

Data Cleaning: Overview and Emerging Challenges

Xu Chu;Ihab F. Ilyas;Sanjay Krishnan;Jiannan Wang.
international conference on management of data (2016)

205 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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