What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Algorithm
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
- Algorithm and related String searching algorithm, Partition and Substring,
- Scalability which intersects with area such as Theoretical computer science.
His Crowdsourcing research integrates issues from Matching, Leverage and Transitive relation.
His research in Artificial intelligence intersects with topics in Machine learning, Maximization and Social network.
His most cited work include:
- CrowdER: crowdsourcing entity resolution (388 citations)
- EASE: an effective 3-in-1 keyword search method for unstructured, semi-structured and structured data (363 citations)
- Comparing stars: on approximating graph edit distance (287 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Data mining (38.92%)
- Information retrieval (30.54%)
- Theoretical computer science (23.95%)
What were the highlights of his more recent work (between 2014-2021)?
- Theoretical computer science (23.95%)
- Data mining (38.92%)
- Artificial intelligence (11.98%)
In recent papers he was focusing on the following fields of study:
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.
Between 2014 and 2021, his most popular works were:
- iCrowd: An Adaptive Crowdsourcing Framework (125 citations)
- QASCA: A Quality-Aware Task Assignment System for Crowdsourcing Applications (112 citations)
- Online topic-aware influence maximization (110 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Algorithm
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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