D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 5,042 169 World Ranking 8424 National Ranking 3886

Research.com Recognitions

Awards & Achievements

2013 - ACM Distinguished Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Database
  • Artificial intelligence
  • Operating system

Il-Yeol Song mainly focuses on Data science, Information retrieval, Big data, Online analytical processing and Unified Modeling Language. The study incorporates disciplines such as Citation analysis, Citation, Scientometrics and Knowledge engineering in addition to Data science. As part of his studies on Information retrieval, Il-Yeol Song frequently links adjacent subjects like Document clustering.

The concepts of his Big data study are interwoven with issues in Variety, Emerging technologies and Knowledge extraction. Online analytical processing is a subfield of Data warehouse that Il-Yeol Song tackles. His Unified Modeling Language study incorporates themes from Data modeling, Use case, Software engineering and Modeling language.

His most cited work include:

  • A survey on ontology mapping (511 citations)
  • A UML profile for multidimensional modeling in data warehouses (249 citations)
  • Analytics over large-scale multidimensional data: the big data revolution! (249 citations)

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

The scientist’s investigation covers issues in Data science, Data warehouse, Online analytical processing, Information retrieval and Database. His Data science study deals with Big data intersecting with Variety. His Data warehouse study is focused on Data mining in general.

In his research on the topic of Data mining, Entity–relationship model, Data modeling, Ternary operation and Binary number is strongly related with Theoretical computer science. His Online analytical processing research incorporates themes from XML, World Wide Web and Business intelligence. His studies deal with areas such as Graph and Natural language processing as well as Information retrieval.

He most often published in these fields:

  • Data science (28.21%)
  • Data warehouse (27.47%)
  • Online analytical processing (22.71%)

What were the highlights of his more recent work (between 2013-2020)?

  • Data science (28.21%)
  • Big data (9.16%)
  • Data warehouse (27.47%)

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

His scientific interests lie mostly in Data science, Big data, Data warehouse, Online analytical processing and Database. The various areas that Il-Yeol Song examines in his Data science study include Open research, Metadata, Data integration and Knowledge extraction. His Big data research is multidisciplinary, relying on both Data modeling, Variety and Visualization.

In his study, Il-Yeol Song carries out multidisciplinary Online analytical processing and Shanghai china research. His work in Database addresses issues such as Distributed File System, which are connected to fields such as Joins, Sargable, Data independence and Concurrency control. His Query language study is concerned with the larger field of Information retrieval.

Between 2013 and 2020, his most popular works were:

  • Big data technologies and Management (103 citations)
  • Big data and data science: what should we teach? (64 citations)
  • Modeling and Management of Big Data (35 citations)

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

  • Database
  • Artificial intelligence
  • Operating system

Il-Yeol Song focuses on Data science, Big data, Database, Online analytical processing and Data warehouse. His work deals with themes such as Social media, Web page and Knowledge extraction, which intersect with Data science. Il-Yeol Song has included themes like Emerging technologies, NoSQL, Software analytics, Variety and Big graph in his Knowledge extraction study.

His study in Big data is interdisciplinary in nature, drawing from both Project management, Key, Information technology and Computational thinking. His Database research is multidisciplinary, incorporating elements of Joins, Sargable and Distributed File System.

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 survey on ontology mapping

Namyoun Choi;Il-Yeol Song;Hyoil Han.
international conference on management of data (2006)

785 Citations

Analytics over large-scale multidimensional data: the big data revolution!

Alfredo Cuzzocrea;Il-Yeol Song;Karen C. Davis.
data warehousing and olap (2011)

428 Citations

A UML profile for multidimensional modeling in data warehouses

Sergio Luján-Mora;Juan Trujillo;Il-Yeol Song.
data and knowledge engineering (2006)

389 Citations

Conceptual Modeling-ER 2003

Il-Yeol Song;Stephen W. Liddle;Tok-Wang Ling;Peter Scheuermann.
(2003)

257 Citations

Data warehousing and OLAP over big data: current challenges and future research directions

Alfredo Cuzzocrea;Ladjel Bellatreche;Il-Yeol Song.
data warehousing and olap (2013)

155 Citations

Big data technologies and Management

Veda C. Storey;Il-Yeol Song.
data and knowledge engineering (2017)

154 Citations

Extending the UML for Multidimensional Modeling

Sergio Luján-Mora;Juan Trujillo;Il-Yeol Song.
Lecture Notes in Computer Science (2002)

147 Citations

Integration of association rules and ontologies for semantic query expansion

Min Song;Il-Yeol Song;Xiaohua Hu;Robert B. Allen.
data and knowledge engineering (2007)

136 Citations

The thematic and citation landscape of Data and Knowledge Engineering (1985-2007)

Chaomei Chen;Il-Yeol Song;Xiaojun Yuan;Jian Zhang.
data and knowledge engineering (2008)

135 Citations

XML-OLAP: a multidimensional analysis framework for XML warehouses

Byung-Kwon Park;Hyoil Han;Il-Yeol Song.
data warehousing and knowledge discovery (2005)

122 Citations

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