What is he best known for?
The fields of study he is best known for:
- Operating system
- Database
- Artificial intelligence
Data mining, Database, Tuple, Scalability and Theoretical computer science are his primary areas of study.
He combines subjects such as SPARQL and Artificial intelligence with his study of Data mining.
In the field of Artificial intelligence, his study on Deep learning overlaps with subjects such as Root cause analysis.
His Database research incorporates themes from Time complexity, Algorithm, Algorithm design, Cryptographic primitive and Spatial query.
Feifei Li has included themes like Task, Data processing and Data set in his Scalability study.
His Theoretical computer science research is multidisciplinary, incorporating elements of Plaintext, Encryption, Cloud computing, Computer security model and Partition.
His most cited work include:
- Approximate aggregation techniques for sensor databases (533 citations)
- Dynamic authenticated index structures for outsourced databases (351 citations)
- DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning (338 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Data mining, Database, Scalability, Cloud computing and Information retrieval.
His research on Data mining focuses in particular on Query optimization.
His work carried out in the field of Query optimization brings together such families of science as Web query classification and Sargable.
His Database study combines topics in areas such as Overhead and Server.
The Scalability study combines topics in areas such as Data management and Big data.
His studies examine the connections between Ranking and genetics, as well as such issues in Ranking, with regards to Probabilistic logic.
He most often published in these fields:
- Data mining (27.81%)
- Database (18.72%)
- Scalability (11.23%)
What were the highlights of his more recent work (between 2018-2021)?
- Database (18.72%)
- Cloud computing (10.16%)
- Data mining (27.81%)
In recent papers he was focusing on the following fields of study:
Feifei Li mainly focuses on Database, Cloud computing, Data mining, Artificial intelligence and Overhead.
His Database study combines topics from a wide range of disciplines, such as Blockchain, Associative array and Hotspot.
The study incorporates disciplines such as Scalability, SQL and Online analytical processing in addition to Cloud computing.
His Analytics study in the realm of Data mining connects with subjects such as Popularity.
In his study, Attenuation and Coding is inextricably linked to Pattern recognition, which falls within the broad field of Artificial intelligence.
His research integrates issues of Data modeling, Data compression and Compression in his study of Overhead.
Between 2018 and 2021, his most popular works were:
- X-Engine: An Optimized Storage Engine for Large-scale E-commerce Transaction Processing (19 citations)
- Narrowing the Gap Between Serverless and its State with Storage Functions (14 citations)
- Spell: Online Streaming Parsing of Large Unstructured System Logs (13 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Database
- Artificial intelligence
His primary scientific interests are in Database, Cloud computing, Database transaction, Transaction processing and Online transaction processing.
Feifei Li interconnects Distributed ledger and Blockchain in the investigation of issues within Database.
His Cloud computing research incorporates elements of Scalability, Distributed computing, Online analytical processing and Asynchronous communication.
As part of his studies on Scalability, Feifei Li frequently links adjacent subjects like Autoscaling.
Feifei Li combines subjects such as Recommender system, Ranking, Information retrieval and Pairwise comparison with his study of Database transaction.
His biological study spans a wide range of topics, including Big data, E-commerce, Transactions per second and Cache.
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