2020 - ACM Fellow For contributions to data cleaning and data integration
2014 - ACM Distinguished Member
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.
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.
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.
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.
A survey of top-k query processing techniques in relational database systems
Ihab F. Ilyas;George Beskales;Mohamed A. Soliman.
ACM Computing Surveys (2008)
Top-k Query Processing in Uncertain Databases
M. A. Soliman;I. F. Ilyas;K. Chen-Chuan Chang.
international conference on data engineering (2007)
Supporting top- k join queries in relational databases
Ihab F. Ilyas;Walid G. Aref;Ahmed K. Elmagarmid.
very large data bases (2004)
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)
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)
NADEEF: a commodity data cleaning system
Michele Dallachiesa;Amr Ebaid;Ahmed Eldawy;Ahmed Elmagarmid.
international conference on management of data (2013)
Holistic data cleaning: Putting violations into context
Xu Chu;I. F. Ilyas;P. Papotti.
international conference on data engineering (2013)
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)
Data Curation at Scale: The Data Tamer System
Michael Stonebraker;Daniel Bruckner;Ihab F. Ilyas;George Beskales.
conference on innovative data systems research (2013)
Data Cleaning: Overview and Emerging Challenges
Xu Chu;Ihab F. Ilyas;Sanjay Krishnan;Jiannan Wang.
international conference on management of data (2016)
Profile was last updated on December 6th, 2021.
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