J
John F. Roddick
Researcher at Flinders University
Publications - 210
Citations - 7334
John F. Roddick is an academic researcher from Flinders University. The author has contributed to research in topics: Knowledge extraction & Association rule learning. The author has an hindex of 39, co-authored 209 publications receiving 7036 citations. Previous affiliations of John F. Roddick include Sewanee: The University of the South & University of South Australia.
Papers
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Journal ArticleDOI
A consensus glossary of temporal database concepts
Curtis E. Dyreson,Fabio Grandi,Wolfgang Käfer,Nick Kline,Nikos A. Lorentzos,Yannis Mitsopoulos,Angelo Montanari,Daniel Nonen,Elisa Peressi,Barbara Pernici,John F. Roddick,Nandlal L. Sarda,Maria Rita Scalas,Arie Segev,Richard T. Snodgrass,Mike D. Soo,Abdullah Uz Tansel,Paolo Tiberio,Gio Wiederhold +18 more
TL;DR: The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts by providing definitions of a wide range of concepts specific to and widely used within temporal databases.
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A survey of temporal knowledge discovery paradigms and methods
TL;DR: The confluence of temporal databases and data mining is investigated, the work to date is surveyed, and the issues involved and the outstanding problems in temporal data mining are explored.
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A survey of schema versioning issues for database systems
TL;DR: The modelling, architectural and query language issues relating to the support of evolving schemata in database systems and the future directions of schema versioning research are discussed.
Journal ArticleDOI
Association mining
Aaron Ceglar,John F. Roddick +1 more
TL;DR: A survey of association mining fundamentals is presented, detailing the evolution of associationmining algorithms from the seminal to the state-of-the-art, that is, itemset identification, rule generation, and their generic optimizations.
Journal ArticleDOI
Sequential pattern mining -- approaches and algorithms
Carl Mooney,John F. Roddick +1 more
TL;DR: This article surveys the approaches and algorithms proposed to date in Sequential Pattern Mining, a subfield of data mining to focus on detecting and analyzing frequent subsequences in data.