What is he best known for?
The fields of study he is best known for:
- Database
- Artificial intelligence
- Operating system
His primary areas of study are Data mining, Artificial intelligence, Schema matching, Information retrieval and Information system.
His Data mining research is multidisciplinary, relying on both Scalability, Event, Complex event processing, Queueing theory and Operations research.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Stable marriage problem.
The concepts of his Schema matching study are interwoven with issues in Star schema, Conceptual schema and Database schema.
His study on Semantic Web is often connected to Ontology as part of broader study in Information retrieval.
His work deals with themes such as Business process, Knowledge management, Human–computer interaction, Blockchain and Information seeking, which intersect with Information system.
His most cited work include:
- Blockchains for Business Process Management - Challenges and Opportunities (230 citations)
- A framework for modeling and evaluating automatic semantic reconciliation (123 citations)
- Managing uncertainty in schema matching with top-k schema mappings (84 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Data mining, Artificial intelligence, Schema matching, Machine learning and Information retrieval.
He interconnects Process mining, Business process, Queueing theory, Event and Information system in the investigation of issues within Data mining.
His Information system research is multidisciplinary, incorporating elements of Web service and Information integration.
His studies link Process modeling with Artificial intelligence.
His research in Schema matching focuses on subjects like Conceptual schema, which are connected to Semi-structured model.
His Ontology and Semantic Web study in the realm of Information retrieval connects with subjects such as Ontology.
He most often published in these fields:
- Data mining (26.85%)
- Artificial intelligence (15.18%)
- Schema matching (12.84%)
What were the highlights of his more recent work (between 2015-2021)?
- Data mining (26.85%)
- Artificial intelligence (15.18%)
- Machine learning (12.06%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Data mining, Artificial intelligence, Machine learning, Business process and Event.
His work on Decision tree as part of general Data mining study is frequently connected to Multi-source, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Artificial intelligence connects with themes related to Schema matching in his study.
His work on Goal recognition as part of general Machine learning study is frequently linked to Observable, bridging the gap between disciplines.
His Event study combines topics from a wide range of disciplines, such as Process modeling, Process, Cluster analysis and Identification.
Avigdor Gal focuses mostly in the field of Business process management, narrowing it down to matters related to The Internet and, in some cases, Data science.
Between 2015 and 2021, his most popular works were:
- Blockchains for Business Process Management - Challenges and Opportunities (230 citations)
- Comparative analysis of approximate blocking techniques for entity resolution (82 citations)
- Traveling time prediction in scheduled transportation with journey segments (50 citations)
In his most recent research, the most cited papers focused on:
- Database
- Artificial intelligence
- Operating system
Avigdor Gal mainly investigates Data mining, Artificial intelligence, Business process, Machine learning and Business process management.
His Data mining research incorporates themes from Process modeling, Process mining, Queueing theory, Blocking and Event.
His research in Artificial intelligence intersects with topics in Schema matching and Pattern recognition.
His research integrates issues of Human-in-the-loop, Consistency and Semantic Web in his study of Schema matching.
His work on Schema as part of general Machine learning research is frequently linked to Observable and Blocking techniques, thereby connecting diverse disciplines of science.
The study incorporates disciplines such as Knowledge management, Field, Autonomous agent, Blockchain and Data science in addition to Business process management.
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