What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Database
Sihem Amer-Yahia mainly investigates XML, Information retrieval, World Wide Web, XML database and Efficient XML Interchange.
His study in XML is interdisciplinary in nature, drawing from both Ranking, Matching, Tree and Pattern matching.
His study focuses on the intersection of Tree and fields such as Key with connections in the field of Algorithm and Theoretical computer science.
His research on Information retrieval focuses in particular on Query language.
His work on Recommender system as part of general World Wide Web research is frequently linked to Attendance, thereby connecting diverse disciplines of science.
His XML database research includes themes of XML Signature, Document Structure Description, XML schema, XML validation and Streaming XML.
His most cited work include:
- Automatic construction of travel itineraries using social breadcrumbs (263 citations)
- Group recommendation: semantics and efficiency (259 citations)
- Minimization of tree pattern queries (224 citations)
What are the main themes of his work throughout his whole career to date?
Information retrieval, World Wide Web, Data science, XML and Crowdsourcing are his primary areas of study.
His Information retrieval research is multidisciplinary, incorporating perspectives in XML validation, Efficient XML Interchange and XML database.
His work on Recommender system, Social web and Social network as part of general World Wide Web study is frequently linked to Diversity, bridging the gap between disciplines.
His Data science study combines topics in areas such as Variety, Social media, Sentence and Data management.
His work carried out in the field of XML brings together such families of science as Query language, Database, Structure and Ranking.
His study in Crowdsourcing is interdisciplinary in nature, drawing from both Approximation algorithm and Set.
He most often published in these fields:
- Information retrieval (29.96%)
- World Wide Web (19.38%)
- Data science (18.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Data science (18.50%)
- Artificial intelligence (9.69%)
- Machine learning (6.61%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Data science, Artificial intelligence, Machine learning, Domain and Analytics.
His Data science research includes themes of Data management, Variety, Semantics, Behavioral analytics and Visualization.
Sihem Amer-Yahia interconnects Graph and Multi-core processor in the investigation of issues within Artificial intelligence.
His study in the fields of Deep learning, Artificial neural network, Missing data and Decision tree under the domain of Machine learning overlaps with other disciplines such as Architecture.
His Analytics research incorporates elements of Entropy, Stratified sampling, Social web and Cohort.
His Social web study results in a more complete grasp of World Wide Web.
Between 2018 and 2021, his most popular works were:
- Optimized group formation for solving collaborative tasks (15 citations)
- Scalable Interactive Dynamic Graph Clustering on Multicore CPUs (10 citations)
- Research response to coronavirus disease 2019 needed better coordination and collaboration: a living mapping of registered trials. (3 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
Sihem Amer-Yahia focuses on Analytics, Social web, Data science, Approximation algorithm and Key.
His Analytics study which covers Data analysis that intersects with Data visualization and Visualization.
The concepts of his Data science study are interwoven with issues in Sentence, Optimization problem and Crowdsourcing.
His Approximation algorithm research incorporates themes from Multi-objective optimization, Theoretical computer science and Group.
His studies in Key integrate themes in fields like Information retrieval, Data structure and Cohort.
His Demographics research overlaps with Domain, World Wide Web, Usability, Publishing and Publication.
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