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 34 Citations 4,987 192 World Ranking 6475 National Ranking 112

Overview

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.

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

Blockchains for Business Process Management - Challenges and Opportunities

Jan Mendling;Ingo Weber;Wil Van Der Aalst;Jan Vom Brocke.
acm transactions on management information systems (2018)

300 Citations

A framework for modeling and evaluating automatic semantic reconciliation

Avigdor Gal;Ateret Anaby-Tavor;Alberto Trombetta;Danilo Montesi.
very large data bases (2005)

169 Citations

Managing uncertainty in schema matching with top-k schema mappings

Avigdor Gal.
Journal on Data Semantics (2006)

139 Citations

Automatic ontology matching using application semantics

Avigdor Gal;Giovanni Modica;Hasan Jamil;Ami Eyal.
Ai Magazine (2005)

123 Citations

Heterogeneous Stream Processing and Crowdsourcing for Urban Traffic Management

Alexander Artikis;Matthias Weidlich;Francois Schnitzler;Ioannis Boutsis.
extending database technology (2014)

120 Citations

Complex event processing over uncertain data

Segev Wasserkrug;Avigdor Gal;Opher Etzion;Yulia Turchin.
distributed event-based systems (2008)

119 Citations

The Use of Machine-Generated Ontologies in Dynamic Information Seeking

Giovanni A. Modica;Avigdor Gal;Avigdor Gal;Hasan M. Jamil.
cooperative information systems (2001)

118 Citations

Uncertain Schema Matching

Avigdor Gal.
(2011)

117 Citations

Comparative analysis of approximate blocking techniques for entity resolution

George Papadakis;Jonathan Svirsky;Avigdor Gal;Themis Palpanas.
very large data bases (2016)

113 Citations

Queue Mining – Predicting Delays in Service Processes

Arik Senderovich;Matthias Weidlich;Avigdor Gal;Avishai Mandelbaum.
conference on advanced information systems engineering (2014)

105 Citations

Best Scientists Citing Avigdor Gal

Ingo Weber

Ingo Weber

Technical University of Berlin

Publications: 37

Wil M. P. van der Aalst

Wil M. P. van der Aalst

RWTH Aachen University

Publications: 35

Jan Mendling

Jan Mendling

Humboldt-Universität zu Berlin

Publications: 32

Marlon Dumas

Marlon Dumas

University of Tartu

Publications: 25

Hajo A. Reijers

Hajo A. Reijers

Utrecht University

Publications: 25

Matthias Weidlich

Matthias Weidlich

Humboldt-Universität zu Berlin

Publications: 24

Marcello La Rosa

Marcello La Rosa

University of Melbourne

Publications: 17

Mathias Weske

Mathias Weske

Hasso Plattner Institute

Publications: 16

Themis Palpanas

Themis Palpanas

Université Paris Cité

Publications: 14

Pavel Shvaiko

Pavel Shvaiko

University of Trento

Publications: 13

Stefanie Rinderle-Ma

Stefanie Rinderle-Ma

Technical University of Munich

Publications: 13

Karl Aberer

Karl Aberer

École Polytechnique Fédérale de Lausanne

Publications: 13

Manfred Reichert

Manfred Reichert

University of Ulm

Publications: 12

Subbarao Kambhampati

Subbarao Kambhampati

Arizona State University

Publications: 12

Vana Kalogeraki

Vana Kalogeraki

Athens University of Economics and Business

Publications: 12

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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