What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Data mining
Nikos Mamoulis spends much of his time researching Data mining, Spatial database, Spatial query, Theoretical computer science and Object.
His research in the fields of Nearest neighbor search overlaps with other disciplines such as Transaction data.
The Spatial database study combines topics in areas such as Temporal database and Cluster analysis.
Nikos Mamoulis interconnects Query language, Database, Spatial network and Search algorithm in the investigation of issues within Spatial query.
His Theoretical computer science research incorporates elements of Scalability, Pairwise comparison and Join.
His work deals with themes such as Identification, Computation and Pattern recognition, which intersect with Object.
His most cited work include:
- Query processing in spatial network databases (575 citations)
- Secure kNN computation on encrypted databases (516 citations)
- On discovering moving clusters in spatio-temporal data (352 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Theoretical computer science, Information retrieval, Algorithm and Spatial query.
His research in Data mining intersects with topics in Object, Ranking, Spatial database and k-nearest neighbors algorithm.
His Object study integrates concerns from other disciplines, such as Temporal database and Tuple.
Within one scientific family, Nikos Mamoulis focuses on topics pertaining to Ranking under Ranking, and may sometimes address concerns connected to Uncertain data.
In the field of Theoretical computer science, his study on Range query overlaps with subjects such as Generalization.
His Spatial query study typically links adjacent topics like Query optimization.
He most often published in these fields:
- Data mining (43.49%)
- Theoretical computer science (20.21%)
- Information retrieval (19.86%)
What were the highlights of his more recent work (between 2016-2021)?
- Information retrieval (19.86%)
- Theoretical computer science (20.21%)
- Data mining (43.49%)
In recent papers he was focusing on the following fields of study:
Nikos Mamoulis mainly investigates Information retrieval, Theoretical computer science, Data mining, Crowdsourcing and Machine learning.
His work on RDF Schema, SPARQL, RDF and Search engine as part of general Information retrieval research is frequently linked to Process, bridging the gap between disciplines.
The concepts of his Theoretical computer science study are interwoven with issues in Pattern matching, Maximum flow problem, Link analysis, Graph and Node.
In general Data mining study, his work on Temporal data mining often relates to the realm of Density based, thereby connecting several areas of interest.
His work in Crowdsourcing tackles topics such as Inference which are related to areas like Categorical variable and Quality.
His Machine learning research integrates issues from End-to-end principle, Pairwise comparison and Artificial intelligence.
Between 2016 and 2021, his most popular works were:
- Heterogeneous Information Network Embedding for Meta Path based Proximity. (54 citations)
- FEXIPRO: Fast and Exact Inner Product Retrieval in Recommender Systems (37 citations)
- Local Suppression and Splitting Techniques for Privacy Preserving Publication of Trajectories (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Database
His main research concerns Recommender system, Information retrieval, Data mining, Quality and Aggregate.
His studies in Recommender system integrate themes in fields like Matrix decomposition, Focus and Artificial intelligence.
His work on Spatial query and Search history as part of his general Information retrieval study is frequently connected to Class and Information source, thereby bridging the divide between different branches of science.
In his papers, Nikos Mamoulis integrates diverse fields, such as Data mining and Density based.
The various areas that Nikos Mamoulis examines in his Quality study include Crowdsourcing, Inference, Categorical variable and Missing data.
His Aggregate study incorporates themes from Field, Sequence, Information privacy and Anonymity.
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