A
Ashraf Aboulnaga
Researcher at Qatar Computing Research Institute
Publications - 105
Citations - 4651
Ashraf Aboulnaga is an academic researcher from Qatar Computing Research Institute. The author has contributed to research in topics: Query optimization & XML database. The author has an hindex of 37, co-authored 103 publications receiving 4213 citations. Previous affiliations of Ashraf Aboulnaga include Khalifa University & University of Waterloo.
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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.
Journal ArticleDOI
Self-tuning histograms: building histograms without looking at data
TL;DR: The experimental results show that self-tuning histograms provide a low-cost alternative to traditional multi-dimensional histograms with little loss of accuracy for data distributions with low to moderate skew.
Journal Article
The Niagara Internet Query System.
Jeffrey F. Naughton,David J. DeWitt,David Maier,Ashraf Aboulnaga,Jianjun Chen,Leonidas Galanis,Jaewoo Kang,Rajasekar Krishnamurthy,Qiong Luo,Naveen Prakash,Ravishankar Ramamurthy,Jayavel Shanmugasundaram,Feng Tian,Kristin Tufte,Stratis D. Viglas,Yuan Wang,Chun Zhang,Bruce Jackson,Anurag Kumar Gupta,Rushan Chen +19 more
TL;DR: To handle infinite streams and data sources with unpredictable rates, the Niagara Internet Query System supports a “get partial” operation on blocking operators in order to produce partial query results, and inserts synchronization packets at critical points in the operator tree to guarantee the consistency of (partial) results.
Proceedings Article
Estimating the Selectivity of XML Path Expressions for Internet Scale Applications
TL;DR: This paper proposes two techniques for estimating the selectivity of simple XML path expressions over complex large-scale XML data as would be handled by Internet-scale applications: path trees and Markov tables.
Proceedings ArticleDOI
Arabesque: a system for distributed graph mining
Carlos H. C. Teixeira,Alexandre J. Fonseca,Marco Serafini,Georgos Siganos,Mohammed J. Zaki,Ashraf Aboulnaga +5 more
TL;DR: Arabesque is presented, the first distributed data processing platform for implementing graph mining algorithms that automates the process of exploring a very large number of subgraphs and defines a high-level filter-process computational model that simplifies the development of scalableGraph mining algorithms.