S
Saravanan Thirumuruganathan
Researcher at Qatar Airways
Publications - 99
Citations - 1876
Saravanan Thirumuruganathan is an academic researcher from Qatar Airways. The author has contributed to research in topics: Tuple & Computer science. The author has an hindex of 20, co-authored 92 publications receiving 1258 citations. Previous affiliations of Saravanan Thirumuruganathan include Qatar Computing Research Institute & University of Texas at Arlington.
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Proceedings ArticleDOI
Distributed representations of tuples for entity resolution
TL;DR: This work proposes a locality sensitive hashing (LSH) based blocking approach that takes all attributes of a tuple into consideration and produces much smaller blocks, compared with traditional methods that consider only a few attributes.
Journal ArticleDOI
Task assignment optimization in knowledge-intensive crowdsourcing
Senjuti Basu Roy,Ioanna Lykourentzou,Saravanan Thirumuruganathan,Sihem Amer-Yahia,Gautam Das +4 more
TL;DR: This work formulates, for the first time, the problem of worker-to-task assignment in KI-C as an optimization problem by proposing efficient adaptive algorithms to solve it and by accounting for human factors, such as worker expertise, wage requirements, and availability inside the optimization process.
Proceedings ArticleDOI
Creating Embeddings of Heterogeneous Relational Datasets for Data Integration Tasks
TL;DR: In this paper, a graph-based representation is proposed to describe a rich set of relationships inherent in the relational world, and sentences are derived from such a graph that effectively describe the similarity across elements (tokens, attributes, rows) in the two datasets.
Journal ArticleDOI
DeepER - Deep Entity Resolution.
TL;DR: This work presents a novel ER system, called DeepER, that achieves good accuracy, high efficiency, as well as ease-of-use, and requires much less human labeled data and does not need feature engineering, compared with traditional machine learning based approaches.
Proceedings ArticleDOI
ZeroER: Entity Resolution using Zero Labeled Examples
TL;DR: ZeroER as mentioned in this paper proposes a generative model based on Gaussian Mixture Models (GMMs) to learn the similarity vectors for matches and unmatches for entity resolution task.