What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Programming language
His primary areas of investigation include Information retrieval, Data mining, Data compression, Search engine indexing and Ranking.
The various areas that Alistair Moffat examines in his Information retrieval study include Signature and Data science.
Within one scientific family, Alistair Moffat focuses on topics pertaining to Inverted index under Data mining, and may sometimes address concerns connected to Pattern recognition.
His research on Data compression concerns the broader Algorithm.
His Search engine indexing research integrates issues from Document retrieval, Full text search, Search engine and Index.
The Ranking study combines topics in areas such as Query expansion, Ranking, Query optimization and Pruning.
His most cited work include:
- Managing Gigabytes: Compressing and Indexing Documents and Images (1640 citations)
- Inverted files for text search engines (940 citations)
- Managing gigabytes (2nd ed.): compressing and indexing documents and images (734 citations)
What are the main themes of his work throughout his whole career to date?
Alistair Moffat focuses on Information retrieval, Data mining, Algorithm, Data compression and Artificial intelligence.
His studies link World Wide Web with Information retrieval.
His Data mining research includes themes of Pruning, Inverted index, Set, Query expansion and Ranking.
His Data compression study combines topics in areas such as Theoretical computer science, Coding and Data compression ratio.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Pattern recognition and Natural language processing.
His Search engine indexing study frequently involves adjacent topics like Document retrieval.
He most often published in these fields:
- Information retrieval (34.46%)
- Data mining (22.46%)
- Algorithm (20.92%)
What were the highlights of his more recent work (between 2016-2021)?
- Information retrieval (34.46%)
- Artificial intelligence (12.92%)
- Data mining (22.46%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Information retrieval, Artificial intelligence, Data mining, Machine learning and Search engine.
His study in the field of Relevance and Ranking is also linked to topics like Metric.
His studies in Artificial intelligence integrate themes in fields like Range, Decoding methods and Natural language processing.
His work is dedicated to discovering how Data mining, Precision and recall are connected with Entropy and other disciplines.
While the research belongs to areas of Machine learning, Alistair Moffat spends his time largely on the problem of Inference, intersecting his research to questions surrounding Confidence interval and Stability.
Alistair Moffat has researched Search engine in several fields, including Task, Load balancing and Information needs.
Between 2016 and 2021, his most popular works were:
- Can machine translation systems be evaluated by the crowd alone (90 citations)
- Incorporating User Expectations and Behavior into the Measurement of Search Effectiveness (30 citations)
- Retrieval Consistency in the Presence of Query Variations (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Statistics
Artificial intelligence, Inverted index, Information needs, Search engine and Decoding methods are his primary areas of study.
His study in Inverted index is interdisciplinary in nature, drawing from both Entropy encoding and Algorithm.
His research integrates issues of Ranking, Information retrieval, Specific-information, User expectations and Residual in his study of Information needs.
His study deals with a combination of Information retrieval and CLARITY.
The study incorporates disciplines such as Boosting, Computation, Data mining and Relevance in addition to Search engine.
Alistair Moffat has included themes like Range, Theoretical computer science and Arithmetic coding in his Decoding methods study.
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