Transition Discovery of Sequential Behaviors in Email Application Usage Using Hidden Markov Models

Robinson, William; Akhlaghi, Arash; Deng, Tianjie

doi:10.1109/hicss.2013.574

Cited by 3 publications

(5 citation statements)

References 44 publications

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“…The software finds the difference of the models to characterize the change: dλ/dt = (λ 2 -λ 1 ) / (t 2 -t 1 ). If the difference Δλ is significant, by some measure, then we have found a transition point [22].…”

Section: Model Differencingmentioning

confidence: 98%

“…The first column presents the concepts, which are defined in the second column, while the third column presents representative references. Simple difference metrics [38]; Markov probability distribution differences [22] 5 Aggregate sequential behavior transition diagnosis…”

Section: Sequence Stream-mining Tool Supportmentioning

confidence: 99%

“…HMMs can be used to identify transitions by: (1) comparing consecutive HMMs generated from the observation sequences, or (2) comparing consecutive acceptance probabilities [22].…”

Section: Hmm Probabilitiesmentioning

confidence: 99%

“…Markov probability differences enumerate as event-sequence metrics [22] 6 Integrative sequence mining…”

Section: Sequence Stream-mining Tool Supportmentioning

confidence: 99%

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Data Mining Behavioral Transitions in Open Source Repositories

Robinson

Deng

2015

2015 48th Hawaii International Conference on System Sciences

Self Cite

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Open-source repository data can be automatically mined using sequence mining methods to provide high-level feedback on project status. GitHub.com projects are acquired, sequence-mined, clustered, and regressed to analyze project characteristics. Such results can be presented to project managers, as part of a display generated by an automated monitoring system. Such monitoring systems provide high-level feedback in real-time. This project is a preliminary step in a larger research project aimed at understanding and monitoring FLOSS projects using this process modeling approach.

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Section: Model Differencingmentioning

confidence: 98%

Section: Sequence Stream-mining Tool Supportmentioning

confidence: 99%

“…HMMs can be used to identify transitions by: (1) comparing consecutive HMMs generated from the observation sequences, or (2) comparing consecutive acceptance probabilities [22].…”

Section: Hmm Probabilitiesmentioning

confidence: 99%

“…Markov probability differences enumerate as event-sequence metrics [22] 6 Integrative sequence mining…”

Section: Sequence Stream-mining Tool Supportmentioning

confidence: 99%

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Data Mining Behavioral Transitions in Open Source Repositories

Robinson

Deng

2015

2015 48th Hawaii International Conference on System Sciences

Self Cite

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“…Papers in this theme focused mainly on human performance improvement, assessment, and technologies, which affect the way organizations build healthcare systems today (Juric, Kim, Panneerselvam, & Tesanovic, 2017). Research topics came from a wide variety of HIT implementation areas such as the modeling of HIT users' behavioral changes (Robinson, Akhlaghi, & Deng, 2013), HIT for process management , and the development of effective healthcare apps on mobile operating platforms (Agarwal et al, 2013). The HIT architectures and implementation theme focused mainly on the architecture and implementation of HIT and, thus, on topics such as personalized medicine (Israelson & Cankaya, 2012;Bravhar & Juric, 2017), predictive HIT (Abidi, Cox, Shepherd, & Abidi, 2012;Rudra, Li, & Kavaki, 2012), HIT for drug administration (Bravhar & Juric, 2017), smart healthcare (Haghigh, Zaslavsky, Krishnaswamy, & Gaber, 2009;Mauro, Sunyaev, Leimeister, Schweiger, & Krcmar, 2008;Green & Young, 2008;Clarke & Steele, 2014), and tracking HIT (e.g., Ryan, Doster, Daily, & Lewis, 2016).…”

Section: Hit Architectures and Implementationmentioning

confidence: 99%

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Samhan¹,

Crampton²,

Ruane³

2018

CAIS

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Research has extensively demonstrated that healthcare industry has rapidly implemented and adopted information technology in recent years. Research in health information technology (HIT), which represents a major component of the Hawaii International Conference on System Sciences, demonstrates similar findings. In this paper, review the literature to better understand the work on HIT that researchers have conducted in HICSS from 2008 to 2017. In doing so, we identify themes, methods, technology types, research populations, context, and emerged research gaps from the reviewed literature. With much change and development in the HIT field and varying levels of adoption, this review uncovers, catalogs, and analyzes the research in HIT at HICSS in this ten-year period and provides future directions for research in the field.

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Developer Behavior and Sentiment from Data Mining Open Source Repositories

Robinson

Deng

2016

2016 49th Hawaii International Conference on System Sciences (HICSS)

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Transition Discovery of Sequential Behaviors in Email Application Usage Using Hidden Markov Models

Cited by 3 publications

References 44 publications

Data Mining Behavioral Transitions in Open Source Repositories

Data Mining Behavioral Transitions in Open Source Repositories

The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Developer Behavior and Sentiment from Data Mining Open Source Repositories

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