What is he best known for?
The fields of study he is best known for:
- Database
- Operating system
- Artificial intelligence
His scientific interests lie mostly in Data mining, Schema matching, Information retrieval, Schema and Data warehouse.
In general Data mining, his work in Information integration is often linked to Product linking many areas of study.
His Schema matching study is concerned with the larger field of Data integration.
His research on Schema also deals with topics like
- Semi-structured model that connect with fields like Document Schema Definition Languages and Data model,
- XML schema and related Theoretical computer science.
The study incorporates disciplines such as Data modeling, Database design, 3-dimensional matching, Blossom algorithm and Relational database in addition to Data warehouse.
His Schema migration study incorporates themes from Star schema and Conceptual schema.
His most cited work include:
- A survey of approaches to automatic schema matching (3112 citations)
- Generic Schema Matching with Cupid (1307 citations)
- Similarity flooding: a versatile graph matching algorithm and its application to schema matching (1264 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Information retrieval, Data mining, Data integration, Database and Ontology.
The concepts of his Information retrieval study are interwoven with issues in Annotation, Set, World Wide Web and Domain.
His work on Schema matching and Data warehouse as part of general Data mining study is frequently connected to Reuse, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Erhard Rahm works mostly in the field of Schema matching, limiting it down to topics relating to Schema migration and, in certain cases, Conceptual schema.
His Data integration study combines topics in areas such as Linked data, Mashup and Information integration.
His study focuses on the intersection of Database and fields such as Distributed computing with connections in the field of Online transaction processing, Cloud computing and Parallel computing.
He most often published in these fields:
- Information retrieval (23.33%)
- Data mining (22.33%)
- Data integration (14.33%)
What were the highlights of his more recent work (between 2017-2021)?
- Scalability (12.00%)
- Theoretical computer science (11.00%)
- Record linkage (5.67%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Scalability, Theoretical computer science, Record linkage, Artificial intelligence and Data mining.
The various areas that Erhard Rahm examines in his Scalability study include Domain, Cluster analysis, Linkage, Data science and Big data.
His Theoretical computer science research includes elements of Graph, Graph, Power graph analysis, Dataflow and Visualization.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Key, Pattern recognition and Natural language processing.
His work in the fields of Data mining, such as Schema matching, intersects with other areas such as Scale.
His Database research integrates issues from Hash function and Implementation.
Between 2017 and 2021, his most popular works were:
- Smart Medical Information Technology for Healthcare (SMITH). (31 citations)
- Big Data Competence Center ScaDS Dresden/Leipzig: Overview and selected research activities (25 citations)
- Using Link Features for Entity Clustering in Knowledge Graphs (23 citations)
In his most recent research, the most cited papers focused on:
- Database
- Operating system
- Artificial intelligence
Erhard Rahm mainly investigates Scalability, Data mining, Record linkage, Cluster analysis and Big data.
His Scalability research is multidisciplinary, incorporating elements of Graph analytics, Graph database, Graph and Theoretical computer science.
His studies deal with areas such as Visual analytics, Data management and Competence as well as Graph analytics.
Erhard Rahm regularly links together related areas like Data integration in his Cluster analysis studies.
His Big data study frequently involves adjacent topics like Data science.
His Privacy preserving research is multidisciplinary, relying on both Information sensitivity, Computer network and Dirty data.
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