What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Artificial intelligence
Masaru Kitsuregawa mainly focuses on Data mining, Information retrieval, Theoretical computer science, Database and Parallel computing.
His work on Association rule learning as part of general Data mining research is frequently linked to Position, thereby connecting diverse disciplines of science.
His work on Web search query and Link analysis as part of general Information retrieval study is frequently connected to Polarity and Unique user, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
The study incorporates disciplines such as Boyer–Moore string search algorithm, Commentz-Walter algorithm, Hash function, Algorithm and Partition in addition to Theoretical computer science.
His work deals with themes such as Management information systems, Information repository and Cache, which intersect with Database.
His research investigates the connection between Parallel computing and topics such as Disk array that intersect with problems in Storage efficiency and Degradation.
His most cited work include:
- Application of hash to data base machine and its architecture (290 citations)
- Keyword Search in Spatial Databases: Towards Searching by Document (245 citations)
- Building Lexicon for Sentiment Analysis from Massive Collection of HTML Documents (220 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Database, World Wide Web, Information retrieval and Distributed computing.
His study in Data mining is interdisciplinary in nature, drawing from both Parallel algorithm, Database transaction, The Internet and Cluster analysis.
His work is connected to Relational database and SQL, as a part of Database.
His World Wide Web study frequently links to other fields, such as Graph.
His research on Information retrieval frequently links to adjacent areas such as Artificial intelligence.
His Distributed computing research includes themes of Scalability and Parallel computing.
He most often published in these fields:
- Data mining (25.86%)
- Database (16.72%)
- World Wide Web (13.62%)
What were the highlights of his more recent work (between 2012-2020)?
- Data mining (25.86%)
- Artificial intelligence (9.31%)
- Database transaction (7.59%)
In recent papers he was focusing on the following fields of study:
His main research concerns Data mining, Artificial intelligence, Database transaction, Database and Information retrieval.
His work on Temporal database as part of general Data mining research is often related to Measure, thus linking different fields of science.
Masaru Kitsuregawa has included themes like Machine learning, Pattern recognition and Natural language processing in his Artificial intelligence study.
Masaru Kitsuregawa focuses mostly in the field of Database transaction, narrowing it down to topics relating to Parallel algorithm and, in certain cases, Scalability.
His Transactional leadership research extends to Database, which is thematically connected.
His Information retrieval study incorporates themes from Basis, Social media and Product.
Between 2012 and 2020, his most popular works were:
- Quantifying Collective Attention from Tweet Stream (54 citations)
- Modeling User Leniency and Product Popularity for Sentiment Classification (35 citations)
- Efficient discovery of periodic-frequent patterns in very large databases (29 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Artificial intelligence
Masaru Kitsuregawa mainly investigates Data mining, Measure, Pruning, Artificial intelligence and Scalability.
His study on Big data is often connected to Process as part of broader study in Data mining.
His research integrates issues of Suffix, Database transaction, Database, Transactional leadership and Task in his study of Pruning.
His Artificial intelligence research is multidisciplinary, incorporating elements of Natural language processing, Machine learning and Pattern recognition.
His work in Scalability tackles topics such as Greedy algorithm which are related to areas like Class.
His Automatic summarization research focuses on Sentence and how it relates to Web page.
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