What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Artificial intelligence
Yun Chi spends much of his time researching Data mining, Artificial intelligence, Machine learning, Social network and Tree.
Particularly relevant to Data stream mining is his body of work in Data mining.
His work in the fields of Artificial intelligence, such as Bayesian inference, Stochastic block model, Posterior probability and Word, intersects with other areas such as Projection.
When carried out as part of a general Machine learning research project, his work on Bayesian network is frequently linked to work in Point estimation, therefore connecting diverse disciplines of study.
His research integrates issues of Time complexity and Blogosphere in his study of Social network.
His Tree study integrates concerns from other disciplines, such as Transaction processing and Data structure.
His most cited work include:
- Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (338 citations)
- Evolutionary spectral clustering by incorporating temporal smoothness (321 citations)
- Combining link and content for community detection: a discriminative approach (305 citations)
What are the main themes of his work throughout his whole career to date?
Yun Chi mainly focuses on Data mining, Information retrieval, Artificial intelligence, Cloud computing and Database.
The various areas that Yun Chi examines in his Data mining study include Tree, Data stream clustering and Correlation clustering, Canopy clustering algorithm.
The concepts of his Information retrieval study are interwoven with issues in Web page, Web mining and Document clustering.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition.
His Machine learning research is multidisciplinary, incorporating elements of Time complexity, Social network and Bayesian inference.
His study in Cloud computing is interdisciplinary in nature, drawing from both Workload and Computer network.
He most often published in these fields:
- Data mining (39.13%)
- Information retrieval (17.39%)
- Artificial intelligence (17.39%)
What were the highlights of his more recent work (between 2012-2018)?
- Cloud computing (15.94%)
- Database (15.94%)
- Distributed computing (11.59%)
In recent papers he was focusing on the following fields of study:
Yun Chi mainly investigates Cloud computing, Database, Distributed computing, Multitenancy and Query optimization.
His Cloud computing study incorporates themes from Workload and Replication.
His Distributed computing research includes elements of Provisioning, Operating system and I/O bound.
Query optimization is the subject of his research, which falls under Data mining.
Yun Chi regularly links together related areas like Dynamic database in his Data mining studies.
His Service-level agreement research integrates issues from Virtualization, Resource allocation and Service.
Between 2012 and 2018, his most popular works were:
- Predicting query execution time: Are optimizer cost models really unusable? (105 citations)
- Towards predicting query execution time for concurrent and dynamic database workloads (58 citations)
- PMAX: tenant placement in multitenant databases for profit maximization (42 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Artificial intelligence
Yun Chi mostly deals with Query optimization, Query expansion, Data mining, Query plan and Online aggregation.
His Query optimization study combines topics from a wide range of disciplines, such as Workload, Scheduling, Dynamic database and Distributed computing.
Yun Chi applies his multidisciplinary studies on Query plan and View in his research.
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