What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Artificial intelligence
Vassilis J. Tsotras focuses on Data mining, Temporal database, Search engine indexing, Database and Information retrieval.
His Data mining research is multidisciplinary, incorporating elements of Scalability, Machine learning, Artificial intelligence, k-nearest neighbors algorithm and Spatial query.
His work in Temporal database covers topics such as Range query which are related to areas like Histogram, Estimator, Kernel density estimation and Query optimization.
His Search engine indexing research is multidisciplinary, relying on both Access method, Theoretical computer science, Index and Cluster analysis.
His research in Database intersects with topics in Object and Perfect hash function.
He has included themes like XML validation, Efficient XML Interchange and Document Structure Description in his Information retrieval study.
His most cited work include:
- On indexing mobile objects (367 citations)
- Comparison of access methods for time-evolving data (305 citations)
- Efficient structural joins on indexed XML documents (277 citations)
What are the main themes of his work throughout his whole career to date?
Vassilis J. Tsotras mostly deals with Data mining, Search engine indexing, Theoretical computer science, Information retrieval and XML.
His Data mining course of study focuses on Scalability and World Wide Web.
His Search engine indexing study incorporates themes from Object, Spatial query, Access method and Joins.
His Theoretical computer science research integrates issues from Tree, Auxiliary memory and Data structure.
His work carried out in the field of Information retrieval brings together such families of science as Database, XML validation, Efficient XML Interchange, Interface and Document Structure Description.
The concepts of his XML study are interwoven with issues in Matching and Parallel computing.
He most often published in these fields:
- Data mining (29.72%)
- Search engine indexing (25.94%)
- Theoretical computer science (22.17%)
What were the highlights of his more recent work (between 2014-2021)?
- Scalability (12.74%)
- Search engine indexing (25.94%)
- Big data (6.60%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Scalability, Search engine indexing, Big data, Information retrieval and Theoretical computer science.
Vassilis J. Tsotras works mostly in the field of Scalability, limiting it down to topics relating to Pattern matching and, in certain cases, Parallel computing.
His study in Search engine indexing is interdisciplinary in nature, drawing from both Transaction time, False positive rate, Database and Dimensionality reduction.
His Big data study combines topics from a wide range of disciplines, such as World Wide Web and Data science.
His studies deal with areas such as Valid time, Tree based and ISAM as well as Information retrieval.
His biological study spans a wide range of topics, including Volume, Set, Data mining and Distributed database.
Between 2014 and 2021, his most popular works were:
- FPGA-based Multithreading for In-Memory Hash Joins (26 citations)
- Algebricks: a data model-agnostic compiler backend for big data languages (19 citations)
- Efficient Computation of Top-k Frequent Terms over Spatio-temporal Ranges (15 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Artificial intelligence
Scalability, Parallel computing, Big data, Active data and World Wide Web are his primary areas of study.
His Scalability study necessitates a more in-depth grasp of Operating system.
His Parallel computing research is multidisciplinary, incorporating elements of Multithreading and Hash function.
His research in Big data tackles topics such as Scalable system which are related to areas like Key.
His work deals with themes such as Spatial analysis, Information retrieval and Range query, which intersect with Key.
Vassilis J. Tsotras has included themes like Hash join and Joins in his Speedup study.
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