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Ihab F. Ilyas
Researcher at University of Waterloo
Publications - 174
Citations - 9504
Ihab F. Ilyas is an academic researcher from University of Waterloo. The author has contributed to research in topics: Query optimization & Ranking (information retrieval). The author has an hindex of 47, co-authored 170 publications receiving 8283 citations. Previous affiliations of Ihab F. Ilyas include Qatar Airways & Khalifa University.
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A survey of top-k query processing techniques in relational database systems
TL;DR: This survey describes and classify top-k processing techniques in relational databases including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions, and shows the implications of each dimension on the design of the underlying techniques.
Proceedings ArticleDOI
Top-k Query Processing in Uncertain Databases
TL;DR: A framework that encapsulates a state space model and efficient query processing techniques to tackle the challenges of uncertain data settings is constructed and it is proved that the techniques are optimal in terms of the number of accessed tuples and materialized search states.
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Supporting top- k join queries in relational databases
TL;DR: A new rank-join algorithm that makes use of the individual orders of its inputs to produce join results ordered on a user-specified scoring function is introduced and implemented inside a prototype database engine based on PREDATOR.
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HoloClean: holistic data repairs with probabilistic inference
TL;DR: A series of optimizations are introduced which ensure that inference over HoloClean's probabilistic model scales to instances with millions of tuples, and yields an average F1 improvement of more than 2× against state-of-the-art methods.
Proceedings ArticleDOI
CORDS: automatic discovery of correlations and soft functional dependencies
TL;DR: CorDS as mentioned in this paper is an efficient and scalable tool for automatic discovery of correlations and soft functional dependencies between columns, which can be used as a data mining tool, producing dependency graphs that are of intrinsic interest.