What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Database
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Recommender system, Information retrieval and Cluster analysis.
The Data mining study combines topics in areas such as Object and Path.
Yannis Manolopoulos interconnects Machine learning, Metric and Pattern recognition in the investigation of issues within Artificial intelligence.
The concepts of his Recommender system study are interwoven with issues in Accuracy improvement, Credibility, Higher-order singular value decomposition and Contrast.
His Information retrieval research incorporates elements of Access method, Metadata and Dimensionality reduction.
His Search engine indexing study deals with Time series database intersecting with Database.
His most cited work include:
- Fast subsequence matching in time-series databases (1608 citations)
- Data Mining techniques for the detection of fraudulent financial statements (404 citations)
- Generalized Hirsch h-index for disclosing latent facts in citation networks (319 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Algorithm, Information retrieval, Search engine indexing and Theoretical computer science are his primary areas of study.
His Data mining study incorporates themes from Access method, Database, Recommender system and Cluster analysis, Artificial intelligence.
Specifically, his work in Recommender system is concerned with the study of Collaborative filtering.
His Artificial intelligence research includes elements of Machine learning and Pattern recognition.
A large part of his Algorithm studies is devoted to Quadtree.
He has researched Search engine indexing in several fields, including Scalability and Data structure.
He most often published in these fields:
- Data mining (30.13%)
- Algorithm (14.63%)
- Information retrieval (13.10%)
What were the highlights of his more recent work (between 2016-2021)?
- Data science (7.86%)
- Data mining (30.13%)
- Information retrieval (13.10%)
In recent papers he was focusing on the following fields of study:
Yannis Manolopoulos mostly deals with Data science, Data mining, Information retrieval, Skyline and Recommender system.
The various areas that Yannis Manolopoulos examines in his Data science study include Scientometrics, Categorization, Data analysis and Big data.
His Data mining research is multidisciplinary, incorporating elements of Context, Access method, Spatial database, Spatial query and Collaborative filtering.
Yannis Manolopoulos works on Information retrieval which deals in particular with Search engine indexing.
His work carried out in the field of Search engine indexing brings together such families of science as Tree based and Data structure.
His studies deal with areas such as Artificial intelligence and Social network as well as Recommender system.
Between 2016 and 2021, his most popular works were:
- Preference dynamics with multimodal user-item interactions in social media recommendation (37 citations)
- A time-aware spatio-textual recommender system (31 citations)
- Recommendations based on a heterogeneous spatio-temporal social network (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Database
Yannis Manolopoulos mainly focuses on Data mining, Recommender system, Information and Computer Science, Information retrieval and Big data.
Yannis Manolopoulos has included themes like Scalability, Theoretical computer science, Social network, Artificial intelligence and Join in his Data mining study.
His Artificial intelligence study frequently draws parallels with other fields, such as Machine learning.
His Recommender system study also includes
- Presentation together with Remote assistance,
- Decision support system which connect with Data type and Temporal database.
His Collaborative filtering study in the realm of Information retrieval interacts with subjects such as Social influence.
The study incorporates disciplines such as Data science, Task and Closest pair of points problem in addition to Big data.
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