His scientific interests lie mostly in Information retrieval, Data mining, String metric, Crowdsourcing and Artificial intelligence. His Information retrieval study combines topics in areas such as XML and Document Structure Description. His work deals with themes such as Intelligent decision support system and Similarity, which intersect with Data mining.
His String metric study also includes fields such as
His primary areas of study are Data mining, Information retrieval, Theoretical computer science, XML and Artificial intelligence. Jianhua Feng combines subjects such as Scalability, Crowdsourcing, Similarity, Graph and Inverted index with his study of Data mining. His study in Information retrieval is interdisciplinary in nature, drawing from both XML validation, Database and XML database.
The study incorporates disciplines such as String metric, Edit distance, Disjoint sets, Nearest neighbor search and Partition in addition to Theoretical computer science. His work on XPath, Efficient XML Interchange and Document Structure Description as part of general XML study is frequently connected to Twig, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The concepts of his Artificial intelligence study are interwoven with issues in Matching, Machine learning and Pattern recognition.
Jianhua Feng mainly investigates Theoretical computer science, Data mining, Artificial intelligence, Crowdsourcing and Similarity. The Theoretical computer science study combines topics in areas such as Time complexity, Disjoint sets, Predicate, Nearest neighbor search and Partition. His Data mining research is multidisciplinary, relying on both Matching, Tree and Pruning.
His research integrates issues of Machine learning and Computer vision in his study of Artificial intelligence. His Crowdsourcing research incorporates themes from Human–machine system and Heuristic. Jianhua Feng interconnects String metric, Information retrieval, Substring and Join in the investigation of issues within Similarity.
Jianhua Feng mostly deals with Crowdsourcing, Data mining, Machine learning, Artificial intelligence and Algorithm. His Crowdsourcing research includes elements of Traffic generation model and Graphical model. Many of his studies involve connections with topics such as Greedy algorithm and Data mining.
His studies deal with areas such as Control and Data science as well as Machine learning. In general Artificial intelligence study, his work on Inference and Leverage often relates to the realm of Location aware, thereby connecting several areas of interest. His study looks at the intersection of Algorithm and topics like Join with Similarity.
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CrowdER: crowdsourcing entity resolution
Jiannan Wang;Tim Kraska;Michael J. Franklin;Jianhua Feng.
very large data bases (2012)
EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data
Guoliang Li;Beng Chin Ooi;Jianhua Feng;Jianyong Wang.
international conference on management of data (2008)
Comparing stars: on approximating graph edit distance
Zhiping Zeng;Anthony K. H. Tung;Jianyong Wang;Jianhua Feng.
very large data bases (2009)
Mining Individual Life Pattern Based on Location History
Yang Ye;Yu Zheng;Yukun Chen;Jianhua Feng.
mobile data management (2009)
Effective keyword search for valuable lcas over xml documents
Guoliang Li;Jianhua Feng;Jianyong Wang;Lizhu Zhou.
conference on information and knowledge management (2007)
Efficient interactive fuzzy keyword search
Shengyue Ji;Guoliang Li;Chen Li;Jianhua Feng.
the web conference (2009)
Can we beat the prefix filtering?: an adaptive framework for similarity join and search
Jiannan Wang;Guoliang Li;Jianhua Feng.
international conference on management of data (2012)
Leveraging transitive relations for crowdsourced joins
Jiannan Wang;Guoliang Li;Tim Kraska;Michael J. Franklin.
international conference on management of data (2013)
Pass-join: a partition-based method for similarity joins
Guoliang Li;Dong Deng;Jiannan Wang;Jianhua Feng.
very large data bases (2011)
Online topic-aware influence maximization
Shuo Chen;Ju Fan;Guoliang Li;Jianhua Feng.
very large data bases (2015)
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