H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 4,715 167 World Ranking 7446 National Ranking 63

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Database
  • Algorithm

His primary areas of study are Data mining, Database, Information retrieval, Data structure and Object. His work on Association rule learning and Table as part of general Data mining study is frequently linked to Information sensitivity, bridging the gap between disciplines. His work on Query optimization and Query language as part of his general Database study is frequently connected to Clickstream, thereby bridging the divide between different branches of science.

His work deals with themes such as Distributed database, Query by Example, Spatial query, Query expansion and View, which intersect with Query optimization. In the field of Information retrieval, his study on Recommender system and Search engine indexing overlaps with subjects such as Polyphony and MIDI. His research investigates the connection between Data structure and topics such as String searching algorithm that intersect with problems in Approximate string matching.

His most cited work include:

  • Mermaid—A front-end to distributed heterogeneous databases (163 citations)
  • A music recommendation system based on music data grouping and user interests (138 citations)
  • Hiding Sensitive Association Rules with Limited Side Effects (132 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Data mining, Information retrieval, Database, Query language and Set. He has researched Data mining in several fields, including Data stream and Probabilistic logic. His Information retrieval study incorporates themes from Object, User interface, Data modeling and Index.

Arbee L. P. Chen combines subjects such as Distributed database, RDF query language, Query optimization, Schema and View with his study of Query language. His Distributed database study combines topics in areas such as Relational database and Theoretical computer science. He has included themes like Algorithm and Tuple in his Set study.

He most often published in these fields:

  • Data mining (28.57%)
  • Information retrieval (23.33%)
  • Database (18.10%)

What were the highlights of his more recent work (between 2012-2021)?

  • Data mining (28.57%)
  • Skyline (5.24%)
  • Set (13.33%)

In recent papers he was focusing on the following fields of study:

Arbee L. P. Chen focuses on Data mining, Skyline, Set, Artificial intelligence and Information retrieval. His Data mining study combines topics from a wide range of disciplines, such as Line segment, Series and Similarity. His Skyline research also works with subjects such as

  • Pruning which is related to area like Data structure, Maximization and Small set,
  • Filter and Product most often made with reference to Parallel processing,
  • Process which intersects with area such as Data point and Parallelism.

The various areas that Arbee L. P. Chen examines in his Set study include Computational complexity theory, Theoretical computer science and Mathematical optimization, Greedy algorithm. His Artificial intelligence research incorporates elements of Social influence, Social media, Machine learning and Depression. His Information retrieval research includes themes of Graph database, String and Mobile device.

Between 2012 and 2021, his most popular works were:

  • Determining k-most demanding products with maximum expected number of total customers (33 citations)
  • Finding $$k$$k most favorite products based on reverse top-$$t$$t queries (28 citations)
  • Discovering leaders from social network by action cascade (16 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Database
  • Algorithm

Arbee L. P. Chen mainly focuses on Artificial intelligence, Data mining, Mobile device, Probabilistic logic and Skyline. His Artificial intelligence research is multidisciplinary, relying on both Cognitive psychology, Social media, Machine learning and Depression. His studies in Machine learning integrate themes in fields like Scalability, Human behavior and Information retrieval.

As part of his studies on Data mining, Arbee L. P. Chen often connects relevant areas like Parallel processing. His Probabilistic logic research is multidisciplinary, incorporating perspectives in Graph, Apriori algorithm and Series. His Skyline research is multidisciplinary, incorporating elements of Process and Filter.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

A music recommendation system based on music data grouping and user interests

Hung-Chen Chen;Arbee L. P. Chen.
conference on information and knowledge management (2001)

223 Citations

Hiding Sensitive Association Rules with Limited Side Effects

Yi-Hung Wu;Chia-Ming Chiang;A.L.P. Chen.
IEEE Transactions on Knowledge and Data Engineering (2007)

203 Citations

Mermaid—A front-end to distributed heterogeneous databases

M. Templeton;D. Brill;S.K. Dao;E. Lund.
Proceedings of the IEEE (1987)

199 Citations

Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window

Chih-Hsiang Lin;Ding-Ying Chiu;Yi-Hung Wu;Arbee L. P. Chen.
siam international conference on data mining (2005)

194 Citations

Discovering nontrivial repeating patterns in music data

Jia-Lien Hsu;Chih-Chin Liu;A.L.P. Chen.
IEEE Transactions on Multimedia (2001)

187 Citations

An efficient approach to discovering knowledge from large databases

Show-Jane Yen;A.L.P. Chen.
international conference on parallel and distributed information systems (1996)

138 Citations

A graph-based approach for discovering various types of association rules

Show-Jane Yen;A.L.P. Chen.
IEEE Transactions on Knowledge and Data Engineering (2001)

136 Citations

Efficient repeating pattern finding in music databases

Jia-Lien Hsu;Arbee L. P. Chen;C.-C. Liu.
conference on information and knowledge management (1998)

135 Citations

Query by rhythm: an approach for song retrieval in music databases

J.C.C. Chen;A.L.P. Chen.
international workshop on research issues in data engineering (1998)

121 Citations

Optimal index and data allocation in multiple broadcast channels

Shou-Chih Lo;A.L.P. Chen.
international conference on data engineering (2000)

121 Citations

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