His primary areas of investigation include Distributed computing, World Wide Web, Computer network, Scalability and Data mining. His Distributed computing study combines topics in areas such as The Internet and Information system. His World Wide Web research includes themes of Quality of service, Key and Information retrieval.
His scientific interests lie mostly in World Wide Web, Information retrieval, Distributed computing, Data mining and Database. The Internet, Web service and Semantic Web are the subjects of his World Wide Web studies. His Information retrieval research focuses on Search engine indexing, Relevance, Web search query, Ranking and Database schema.
His research in Distributed computing intersects with topics in Overlay, Overlay network, Scalability and Computer network. The various areas that Karl Aberer examines in his Data mining study include Data modeling and Wireless sensor network.
Karl Aberer spends much of his time researching Artificial intelligence, Data mining, Data science, World Wide Web and Internet privacy. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning and Natural language processing. His Data mining research integrates issues from Data modeling, Variety, Information retrieval and Time series.
His studies in Information retrieval integrate themes in fields like Event, Encoder and Set. Karl Aberer has included themes like Quality, Web application, Scalability and Crowdsourcing in his Data science study. His World Wide Web study integrates concerns from other disciplines, such as Ontology and Credibility.
His main research concerns Data science, Data mining, Crowdsourcing, Information retrieval and Artificial intelligence. His research integrates issues of Data modeling, Variety, Heuristic and Time series in his study of Data mining. His studies deal with areas such as Quality, Structure and Ranking as well as Crowdsourcing.
Karl Aberer has researched Information retrieval in several fields, including Event, Set and Information needs. His Artificial intelligence research includes elements of Machine learning and Series. His Scalability study frequently draws parallels with other fields, such as Distributed computing.
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.
Managing trust in a peer-2-peer information system
Karl Aberer;Zoran Despotovic.
conference on information and knowledge management (2001)
P-Grid: A Self-Organizing Access Structure for P2P Information Systems
cooperative information systems (2001)
P-Grid: a self-organizing structured P2P system
Karl Aberer;Philippe Cudré-Mauroux;Anwitaman Datta;Zoran Despotovic.
international conference on management of data (2003)
CrossFlow: Cross-Organizational Workflow Management in Dynamic Virtual Enterprises
P.W.P.J. Grefen;Karl Aberer;Yigal Hoffner;Heiko Ludwig.
Computer Systems: Science & Engineering (2000)
Infrastructure for Data Processing in Large-Scale Interconnected Sensor Networks
K. Aberer;M. Hauswirth;A. Salehi.
mobile data management (2007)
QoS-Based service selection and ranking with trust and reputation management
Le-Hung Vu;Manfred Hauswirth;Karl Aberer.
international conference on move to meaningful internet systems (2005)
A middleware for fast and flexible sensor network deployment
Karl Aberer;Manfred Hauswirth;Ali Salehi.
very large data bases (2006)
Outtweeting the twitterers - predicting information cascades in microblogs
Wojciech Galuba;Karl Aberer;Dipanjan Chakraborty;Zoran Despotovic.
workshop on online social networks (2010)
Energy-Efficient Continuous Activity Recognition on Mobile Phones: An Activity-Adaptive Approach
Zhixian Yan;Vigneshwaran Subbaraju;Dipanjan Chakraborty;Archan Misra.
international symposium on wearable computers (2012)
GridVine: building internet-scale semantic overlay networks
Karl Aberer;Philippe Cudré-Mauroux;Manfred Hauswirth;Tim Van Pelt.
international semantic web conference (2004)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: