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.
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