What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Algorithm
Jianzhong Li focuses on Data mining, Wireless sensor network, Subgraph isomorphism problem, Graph and Algorithm.
His Data mining research includes elements of Scalability, Tuple, Set, Semantics and Data integrity.
His Wireless sensor network study combines topics in areas such as Distributed algorithm, Distributed computing, Energy consumption, Algorithm design and Key distribution in wireless sensor networks.
His Subgraph isomorphism problem research is multidisciplinary, incorporating perspectives in Time complexity, Induced subgraph isomorphism problem, Graph and Factor-critical graph.
His studies in Graph integrate themes in fields like Theoretical computer science, Mathematical optimization and Bounded function.
His Algorithm study incorporates themes from Linear regression, Missing data, Artificial intelligence and Pattern recognition.
His most cited work include:
- Efficient subgraph matching on billion node graphs (256 citations)
- Graph pattern matching: from intractable to polynomial time (246 citations)
- Distributed Data Aggregation Scheduling in Wireless Sensor Networks (234 citations)
What are the main themes of his work throughout his whole career to date?
Jianzhong Li mainly focuses on Data mining, Algorithm, Wireless sensor network, Theoretical computer science and Distributed computing.
His Data mining research focuses on Data quality and how it connects with Big data.
His Algorithm research incorporates elements of Sampling, Data warehouse and Join.
His research integrates issues of Energy consumption, Key distribution in wireless sensor networks, Real-time computing and Approximation algorithm in his study of Wireless sensor network.
His study looks at the relationship between Theoretical computer science and fields such as Graph, as well as how they intersect with chemical problems.
His research investigates the connection with Distributed computing and areas like Scheduling which intersect with concerns in Schedule.
He most often published in these fields:
- Data mining (28.65%)
- Algorithm (22.31%)
- Wireless sensor network (17.50%)
What were the highlights of his more recent work (between 2018-2021)?
- Algorithm (22.31%)
- Data mining (28.65%)
- Time complexity (6.54%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Algorithm, Data mining, Time complexity, Machine learning and Artificial intelligence.
His Algorithm research incorporates themes from Sampling, Distributed database and Big data.
His Data mining study integrates concerns from other disciplines, such as Tuple, Missing data, Pruning, Database transaction and Data quality.
His work deals with themes such as Relational database and Set, which intersect with Tuple.
The Time complexity study which covers k-nearest neighbors algorithm that intersects with Upper and lower bounds.
His study in the field of Hyperparameter and Hyperparameter optimization is also linked to topics like Cash.
Between 2018 and 2021, his most popular works were:
- Dominating Sets Construction in RF-based Battery-free Sensor Networks with Full Coverage Guarantee (12 citations)
- Distributed Energy-Adaptive Aggregation Scheduling with Coverage Guarantee For Battery-Free Wireless Sensor Networks (12 citations)
- The computation of optimal subset repairs (9 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Algorithm
His primary areas of study are Data mining, Artificial intelligence, Machine learning, Algorithm and Wireless sensor network.
His research in Data mining intersects with topics in Data quality, Information retrieval and Pruning.
The various areas that Jianzhong Li examines in his Artificial intelligence study include Sketch, Information engineering and Measure.
His Machine learning research includes themes of Crowdsourcing, Data collection and Bayesian inference.
His study in Algorithm is interdisciplinary in nature, drawing from both Control, Factor, Scale and Knowledge graph.
His Wireless sensor network research is multidisciplinary, incorporating elements of Network architecture, Distributed generation, Computer engineering, Snapshot and Distributed algorithm.
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