John Roddick

John Roddick

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Research Focus & Metrics

Academic Career Length

35Years (1992-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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7 August 2002 in IEEE Transactions on Knowledge and Data Engineering
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Journal Article
Closed access
JRJohn Roddick
John Roddick
MSM. Spiliopoulou
M. Spiliopoulou
With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining.

1 February 2013 in ACM Computing Surveys
Verified
Journal Article
Open access
CMCarl H. Mooney
Carl H. Mooney
JRJohn Roddick
John Roddick
Sequences of events, items, or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyze frequent subsequences is a common problem. Sequential Pattern Mining arose as a subfield of data mining to focus on this field. This article surveys the approaches and algorithms proposed to date.

1 January 1998
Verified
Book Chapter
Closed access
PDF file
CJChristian S. Jensen
Christian S. Jensen
CDCurtis E. Dyreson
Curtis E. Dyreson
MBMichael Böhlen
Michael Böhlen
JCJames Clifford
James Clifford
RERamez Elmasri
Ramez Elmasri
GWGio Wiederhold
Gio Wiederhold

25 July 2006 in ACM Computing Surveys
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Journal Article
Open access
ACAaron Ceglar
Aaron Ceglar
JRJohn Roddick
John Roddick
The task of finding correlations between items in a dataset, association mining, has received considerable attention over the last decade. This article presents a survey of association mining fundamentals, detailing the evolution of association mining algorithms from the seminal to the state-of-the-art. This survey focuses on the fundamental principles of association mining, that is, itemset identification, rule generation, and their generic optimizations.

1 January 1995 in Information and Software Technology
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Journal Article
Closed access
JRJohn Roddick
John Roddick

1 March 1994 in ACM SIGMOD Record
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Journal Article
Open access
CDCurtis Dyreson
Curtis Dyreson
FGFabio Grandi
Fabio Grandi
WKWolfgang Käfer
Wolfgang Käfer
NKNick Kline
Nick Kline
NLNikos Lorentzos
Nikos Lorentzos
GWGio Wiederhold
Gio Wiederhold
This document contains definitions of a wide range of concepts specific to and widely used within temporal databases. In addition to providing definitions, the document also includes separate explanations of many of the defined concepts. Two sets of criteria are included. First, all included concepts were required to satisfy four relevance criteria, and, second, the naming of the concepts was resolved using a set of evaluation criteria. The concepts are grouped into three categories: concepts of general database interest, of temporal database interest, and of specialized interest. This document is a digest of a full version of the glossary 1 . In addition to the material included here, the full version includes substantial discussions of the naming of the concepts.The consensus effort that lead to this glossary was initiated in Early 1992. Earlier status documents appeared in March 1993 and December 1992 and included terms proposed after an initial glossary appeared in SIGMOD Record in September 1992. The present glossary subsumes all the previous documents. It was most recently discussed at the "ARPA/NSF International Workshop on an Infrastructure for Temporal Databases," in Arlington, TX, June 1993, and is recommended by a significant part of the temporal database community. The glossary meets a need for creating a higher degree of consensus on the definition and naming of temporal database concepts.