H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 61 Citations 20,660 271 World Ranking 1490 National Ranking 40

Overview

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

Managing Gigabytes: Compressing and Indexing Documents and Images

I.H. Witten;A. Moffat;T.C. Bell.
(1999)

3843 Citations

Inverted files for text search engines

Justin Zobel;Alistair Moffat.
ACM Computing Surveys (2006)

1483 Citations

Managing gigabytes (2nd ed.): compressing and indexing documents and images

Ian H. Witten;Alistair Moffat;Timothy C. Bell.
(1999)

1158 Citations

Arithmetic coding revisited

Alistair Moffat;Radford M. Neal;Ian H. Witten.
ACM Transactions on Information Systems (1998)

789 Citations

Implementing the PPM data compression scheme

A. Moffat.
IEEE Transactions on Communications (1990)

597 Citations

Off-line dictionary-based compression

N.J. Larsson;A. Moffat.
Proceedings of the IEEE (2000)

562 Citations

Self-indexing inverted files for fast text retrieval

Alistair Moffat;Justin Zobel.
ACM Transactions on Information Systems (1996)

544 Citations

Exploring the similarity space

Justin Zobel;Alistair Moffat.
international acm sigir conference on research and development in information retrieval (1998)

527 Citations

Inverted files versus signature files for text indexing

Justin Zobel;Alistair Moffat;Kotagiri Ramamohanarao.
ACM Transactions on Database Systems (1998)

503 Citations

Rank-biased precision for measurement of retrieval effectiveness

Alistair Moffat;Justin Zobel.
ACM Transactions on Information Systems (2008)

453 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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