His main research concerns Data mining, Computer security, Location-based service, Theoretical computer science and Nearest neighbor search. In general Data mining, his work in Relational database is often linked to Spatial database linking many areas of study. In general Computer security study, his work on Attack model, Anonymity and Privacy software often relates to the realm of Association, thereby connecting several areas of interest.
His Location-based service research includes themes of Overhead and Mobile device. His research in Theoretical computer science tackles topics such as Heuristic which are related to areas like Semantics, Matching, Graph and Friend of a friend. His work carried out in the field of Nearest neighbor search brings together such families of science as Ranking, Similarity measure and Edit distance.
Panos Kalnis mostly deals with Data mining, Theoretical computer science, Scalability, Database and Information retrieval. His Data mining study combines topics in areas such as Cluster analysis and Anonymity. Panos Kalnis has included themes like SPARQL, RDF, Approximation algorithm, Query optimization and Graph in his Theoretical computer science study.
His Scalability research is multidisciplinary, relying on both Parallel computing, Distributed computing, Peer-to-peer and Server. His Database research integrates issues from Private information retrieval and Encryption. In his research, Overhead is intimately related to Mobile device, which falls under the overarching field of Encryption.
Panos Kalnis mainly focuses on Artificial intelligence, Deep learning, Algorithm, Data science and Machine learning. His Artificial intelligence study incorporates themes from Graph and Linear model. His Deep learning research includes themes of Technical report and Multimedia.
His work in the fields of Computation overlaps with other areas such as Macromolecular docking. His work on Analytics as part of general Data science research is frequently linked to Smart city and Self driving, bridging the gap between disciplines. His Machine learning study integrates concerns from other disciplines, such as Key and Benchmark.
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Algorithm, Machine learning and Technical report. Workload and Process are fields of study that overlap with his Artificial intelligence research. His studies in Deep learning integrate themes in fields like Graph, Linear model and Graph.
His Computation and Quantization study in the realm of Algorithm connects with subjects such as Implementation. His research in Machine learning intersects with topics in Key and Benchmark. The study of Technical report is intertwined with the study of Multimedia in a number of ways.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Private queries in location based services: anonymizers are not necessary
Gabriel Ghinita;Panos Kalnis;Ali Khoshgozaran;Cyrus Shahabi.
international conference on management of data (2008)
Preventing Location-Based Identity Inference in Anonymous Spatial Queries
P. Kalnis;G. Ghinita;K. Mouratidis;D. Papadias.
IEEE Transactions on Knowledge and Data Engineering (2007)
PRIVE: anonymous location-based queries in distributed mobile systems
Gabriel Ghinita;Panos Kalnis;Spiros Skiadopoulos.
the web conference (2007)
On discovering moving clusters in spatio-temporal data
Panos Kalnis;Nikos Mamoulis;Spiridon Bakiras.
symposium on large spatial databases (2005)
Efficient OLAP Operations in Spatial Data Warehouses
Dimitris Papadias;Panos Kalnis;Jun Zhang;Yufei Tao.
symposium on large spatial databases (2001)
Privacy-preserving anonymization of set-valued data
Manolis Terrovitis;Nikos Mamoulis;Panos Kalnis.
very large data bases (2008)
Mizan: a system for dynamic load balancing in large-scale graph processing
Zuhair Khayyat;Karim Awara;Amani Alonazi;Hani Jamjoom.
european conference on computer systems (2013)
Fast data anonymization with low information loss
Gabriel Ghinita;Panagiotis Karras;Panos Kalnis;Nikos Mamoulis.
very large data bases (2007)
GraMi: frequent subgraph and pattern mining in a single large graph
Mohammed Elseidy;Ehab Abdelhamid;Spiros Skiadopoulos;Panos Kalnis.
very large data bases (2014)
Quality and efficiency in high dimensional nearest neighbor search
Yufei Tao;Ke Yi;Cheng Sheng;Panos Kalnis.
international conference on management of data (2009)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: