What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Data mining
Themis Palpanas spends much of his time researching Data mining, Series, Search engine indexing, Theoretical computer science and Index.
His Data mining research includes themes of Context, Cluster analysis and Data set.
Themis Palpanas focuses mostly in the field of Context, narrowing it down to topics relating to Outlier and, in certain cases, Wireless sensor network.
His Search engine indexing study combines topics from a wide range of disciplines, such as Approximation algorithm and Automatic summarization.
His Theoretical computer science research is multidisciplinary, incorporating perspectives in Representation, Graph and Graph.
As a part of the same scientific family, he mostly works in the field of Index, focusing on Information retrieval and, on occasion, Attribute domain and Join.
His most cited work include:
- Online outlier detection in sensor data using non-parametric models (339 citations)
- Survey on mining subjective data on the web (270 citations)
- iSAX 2.0: Indexing and Mining One Billion Time Series (159 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Data mining, Data science, Search engine indexing, Nearest neighbor search and Information retrieval.
By researching both Data mining and Series, Themis Palpanas produces research that crosses academic boundaries.
Themis Palpanas works mostly in the field of Data science, limiting it down to topics relating to Sentiment analysis and, in certain cases, World Wide Web.
His research investigates the connection between Search engine indexing and topics such as State that intersect with issues in Sorting.
His research in Nearest neighbor search intersects with topics in Algorithm and Scalability.
His Query language study, which is part of a larger body of work in Information retrieval, is frequently linked to Schema and Data type, bridging the gap between disciplines.
He most often published in these fields:
- Data mining (28.71%)
- Data science (17.22%)
- Search engine indexing (16.75%)
What were the highlights of his more recent work (between 2018-2021)?
- Nearest neighbor search (15.79%)
- Search engine indexing (16.75%)
- Data mining (28.71%)
In recent papers he was focusing on the following fields of study:
Nearest neighbor search, Search engine indexing, Data mining, Series and Data series are his primary areas of study.
Themis Palpanas interconnects Terminology, Scalability and Key in the investigation of issues within Nearest neighbor search.
His research on Search engine indexing concerns the broader Information retrieval.
His studies deal with areas such as End-to-end principle and Graph query as well as Information retrieval.
His Data mining research is multidisciplinary, relying on both Context, Field and Similarity.
His work in Field addresses issues such as Blocking, which are connected to fields such as Big data.
Between 2018 and 2021, his most popular works were:
- Comparing Similarity Perception in Time Series Visualizations (30 citations)
- Return of the Lernaean Hydra: experimental evaluation of data series approximate similarity search (23 citations)
- Report on the First and Second Interdisciplinary Time Series Analysis Workshop (ITISA) (22 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Database
His scientific interests lie mostly in Search engine indexing, Nearest neighbor search, Data mining, Data series and Artificial intelligence.
Search engine indexing is a primary field of his research addressed under Information retrieval.
His biological study spans a wide range of topics, including JSON and Data analysis.
His Data mining research integrates issues from Context, Field and Similarity.
His work is dedicated to discovering how Field, Blocking are connected with Big data and other disciplines.
His work on Dynamic time warping and Creative visualization as part of his general Artificial intelligence study is frequently connected to Perception, thereby bridging the divide between different branches of science.
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