What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Operating system
Matthias Klusch mainly investigates The Internet, World Wide Web, Intelligent agent, Data mining and Multi-agent system.
His study in The Internet is interdisciplinary in nature, drawing from both Data science, Business operations, Software agent and Internet privacy.
Service and Information retrieval is closely connected to Interoperability in his research, which is encompassed under the umbrella topic of Software agent.
His studies deal with areas such as Semantic Web Rule Language and OWL-S, Social Semantic Web, Semantic Web Stack as well as Service.
His World Wide Web study incorporates themes from Information quality, Knowledge management, Resource, Information engineering and Automated information system.
In his study, Distributed algorithm, Key and Knowledge extraction is strongly linked to Cluster analysis, which falls under the umbrella field of Data mining.
His most cited work include:
- Automated semantic web service discovery with OWLS-MX (493 citations)
- Dynamic service matchmaking among agents in open information environments (477 citations)
- Larks: Dynamic Matchmaking Among Heterogeneous Software Agents in Cyberspace (449 citations)
What are the main themes of his work throughout his whole career to date?
Matthias Klusch focuses on World Wide Web, Semantic Web, Data mining, The Internet and Software engineering.
His biological study spans a wide range of topics, including Intelligent agent, Mobile computing and Knowledge management.
Within one scientific family, Matthias Klusch focuses on topics pertaining to Distributed computing under Intelligent agent, and may sometimes address concerns connected to Multi-agent system.
His research in Semantic Web tackles topics such as Web service which are related to areas like Ontology.
His The Internet study frequently links to adjacent areas such as Software agent.
His Social Semantic Web research is classified as research in Information retrieval.
He most often published in these fields:
- World Wide Web (22.28%)
- Semantic Web (13.99%)
- Data mining (12.95%)
What were the highlights of his more recent work (between 2014-2021)?
- Software engineering (11.40%)
- Process (6.22%)
- Artificial intelligence (9.84%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Software engineering, Process, Artificial intelligence, Business Process Model and Notation and Business process modeling.
His research in the fields of Domain and Data modeling overlaps with other disciplines such as German.
In his research, State, Benchmark, Trajectory and Synthetic data is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
Matthias Klusch interconnects Quality of service and Service in the investigation of issues within Business process modeling.
His research investigates the connection with Service and areas like Agile software development which intersect with concerns in Computer-integrated manufacturing.
His study with Semantic Web involves better knowledge in Information retrieval.
Between 2014 and 2021, his most popular works were:
- Semantic Web Service Search: A Brief Survey (32 citations)
- CDM-Core: A Manufacturing Domain Ontology in OWL2 for Production and Maintenance (9 citations)
- FCE4BPMN: On-demand QoS-based optimised process model execution in the cloud (8 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Operating system
His scientific interests lie mostly in Process, Business Process Model and Notation, Software engineering, Machine learning and Artificial intelligence.
His Process research is multidisciplinary, incorporating elements of Business model, Cloud computing, Service and Component.
His Service research incorporates elements of Agile software development, Computer-integrated manufacturing, Process management, Quality of service and Flexibility.
His Business Process Model and Notation research is multidisciplinary, incorporating elements of Scalability, Distributed computing and Task.
The study incorporates disciplines such as Systems engineering, Cyber-physical system and Fault detection and isolation in addition to Software engineering.
His Machine learning study combines topics from a wide range of disciplines, such as State and SAFER.
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