Adaptation of Multimedia Presentations for Different Display Sizes in the Presence of Preferences and Temporal Constraints

Nimmagadda, Yamini; Kumar, K. Dilip; Lu, Yung-Hsiang

doi:10.1109/tmm.2010.2052024

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…According to the classification of a content adaptation system defined in Fudzee and Abawajy (2008)), and based on comparison methods used in Nimmagadda et al (2010), the methods employed as the criteria in this paper can be defined. These definitions can be based on what "media format" is to be adapted to support what purposes ("general" or "learning environment"), and how to adapt content ("layout generation" and "semantic preservation") to what context ("device," "network," or "user preference").…”

Section: Discussion Of the Personalized Learning Content Adaptation Mmentioning

confidence: 99%

“…Most existing research and systems either separately or partially considered the features of mobile devices, network conditions, and learner preferences (Zhao et al 2008;Nimmagadda et al 2010). Therefore, a pressing issue is how to consider not only the mobile devices' capabilities, but also the available network conditions and learners' diverse preferences in order to efficiently provide properly personalized learning content in mobile learning environments (Tong et al 2006;Mylonas et al 2007;Basaeed et al 2007;Zhao et al 2008;Franzoni et al 2008;Muntean 2008;Pettersson and Gil 2010;Nimmagadda et al 2010).…”

Section: Introductionmentioning

confidence: 99%

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A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

Tseng

Lin

et al. 2011

User Model User-Adap Inter

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With the heterogeneous proliferation of mobile devices, the delivery of learning materials on such devices becomes subject to more and more requirements. Personalized learning content adaptation, therefore, becomes increasingly important to meet the diverse needs imposed by devices, users, usage contexts, and infrastructure. Historical server logs offer a wealth of information on hardware capabilities, learners' preferences, and network conditions, which can be utilized to respond to a new user request with the personalized learning content created from a previous similar request. In this paper, we propose a Personalized Learning Content Adaptation Mechanism (PLCAM), which applies data mining techniques, including clustering and decision tree approaches, to efficiently manage a large number of historical learners' requests. The proposed method will intelligently and directly deliver proper personalized learning content with higher fidelity from the Sharable Content Object Reference Model (SCORM)-compliant Learning Object Repository (LOR) by means of the proposed adaptation decision and content synthesis processes. Furthermore, the experimental results indicate that it is efficient and is expected to prove beneficial to learners.

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Section: Discussion Of the Personalized Learning Content Adaptation Mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A personalized learning content adaptation mechanism to meet diverse user needs in mobile learning environments

Tseng

Lin

et al. 2011

User Model User-Adap Inter

View full text Add to dashboard Cite

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“…The mobile users can negotiate their desired service levels and achieve them by using one or more interfaces. Nimmagadda, Kumar and Lu [58] proposed an adaptation of the presentations based on preferences and temporal constraints. The layout is generated by computing the locations, starting times, and durations of the media files.…”

Section: Heterogeneitymentioning

confidence: 99%

Quality of service provision in mobile multimedia - a survey

Luo

Shyu

2011

Hum. Cent. Comput. Inf. Sci.

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The prevalence of multimedia applications has drastically increased the amount of multimedia data. With the drop of the hardware cost, more and more mobile devices with higher capacities are now used. The widely deployed wireless LAN and broadband wireless networks provide the ubiquitous network access for multimedia applications. Provision of Quality of Service (QoS) is challenging in mobile ad hoc networks because of the dynamic characteristics of mobile networks and the limited resources of the mobile devices. The wireless network is not reliable due to node mobility, multi-access channel and multi-hop communication. In this paper, we provide a survey of QoS provision in mobile multimedia, addressing the technologies at different network layers and cross-layer design. This paper focuses on the QoS techniques over IEEE 802.11e networks. We also provide some thoughts about the challenges and directions for future research.

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“…They adopted the concept of dynamic cache categories and proposed a new replacement algorithm for caches. Nimmagadda et al (2010) presented a content adaptation method for multimedia presentations constituting media files with different start times and durations. They performed adaptation based on preferences and temporal constraints specified by authors and generate an order of importance among media files.…”

Section: Related Workmentioning

confidence: 99%