Vasiliki Kantere

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Vasiliki Kantere
Professor

Room: STE 5060
Office: 613-562-5800 ext. 6708
Work E-mail: vkantere@uOttawa.ca

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Biography

Vasiliki Kantere is a professor at the School of Electrical Engineering and Computer Science, in the Faculty of Engineering. She has also been a Senior Assistant Professor at the School of Electrical and Computer Engineering at the National Technical University of Athens, a Maître d’ Enseignement et de Recherche and a Maître Assistante at the University of Geneva, a Junior Assistant Professor at the Department of Electrical Engineering and Information Technology, Faculty of Engineering and Technology, Cyprus University Of Technology, and a Postdoctoral Researcher at the École Polytechnique Fédérale de Lausanne.


Her general domain of research is Data Management and her main research interests are summarized as follows:

  • Big Data Management: data storage/replication/movement, analytics workflows, execution optimization, deployment optimization, computation-intensive queries, scientific data management, integration and handling of intermediate results, consolidation of heterogeneous data sources, data navigation, query calibration, workflow recalibration techniques, deployment on hybrid infrastructures, time-series data management
  • Big Data Analytics: business and scientific analytics, creation and support of analytics applications: on web data, sensor data, cell data, time-series data, combinations of unstructured and structured data, support of analytics for users of variable expertise, approximate querying, dynamic and agile schema mappings, dynamic and agile data integration and analysis, large data graphs, exploration and visualization, community detection, conversational data analysis
  • Machine Learning: large-scale resource management based on machine learning techniques, adaptations of reinforcement learning techniques, employment of machine learning for using large data on resource management, machine and deep learning techniques for time-series data management, machine and deep learning techniques for time-series data management for query optimization
  • Cloud Computing: data services, cloud economy, cost-aware data management, database-as-a-service, metadata management, data placement, data privacy and reliability, data and process consolidation, cost and time efficiency, service level agreements
  • P2P Systems: P2P overlays structured and unstructured, multidimensional data sharing, peer data management systems, data and processing heterogeneity, query processing, query answering and rewriting, continuous queries, mobile peer environments, data privacy
  • Semantic Web: semantic query processing, semantic similarity, ontology mapping, semantic annotation, semantic clustering, semantic integration
  • Distributed Systems: distributed and federated databases, grid computing, massive information sharing, P2P systems, distributed query processing, query optimization, distributed triggers, distributed data coordination, cloud computing, social networks
  • Databases:query languages, active mechanisms, query optimization, querying on semi-structured and unstructured data, data and schema heterogeneity and integration, schema mapping and data exchange, graph data models 

General: spatial data and queries, information integration and exchange, mobile databases, data streams, privacy and security, sensor networks, querying and processing graphs, location-based services

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