Data preloading technique using intention prediction

Seungyup Lee, Juwan Yoo, Da Young Ju

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Various smart devices provide fast response time and ubiquitous web-environment to users for better user experiences (UXs). However, high device performance that users perceive is not always promised because there should be limited network bandwidth, and computation capabilities. When the network and computation capabilities are overloaded, users experience buffering and loading time to accomplish a certain task. We, therefore, propose data preloading technique [1], which predicts user intention and preloads the web and local application data to provide better device performance in spite of poor network conditions and outdated hardware. We also design intention cognitive model to predict user intention precisely. Four user intention prediction algorithms, which are applicable to various conventional input methods, are described and compared each performance in both user's and device's aspects.

Original languageEnglish
Title of host publicationHuman-Computer Interaction
Subtitle of host publicationApplications and Services - 16th International Conference, HCI International 2014, Proceedings
PublisherSpringer Verlag
Pages32-41
Number of pages10
EditionPART 3
ISBN (Print)9783319072265
DOIs
Publication statusPublished - 2014 Jan 1
Event16th International Conference on Human-Computer Interaction: Applications and Services, HCI International 2014 - Heraklion, Crete, Greece
Duration: 2014 Jun 222014 Jun 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8512 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other16th International Conference on Human-Computer Interaction: Applications and Services, HCI International 2014
CountryGreece
CityHeraklion, Crete
Period14/6/2214/6/27

Fingerprint

Prediction
User Experience
Hardware
Bandwidth
Predict
Cognitive Models
Response Time

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Lee, S., Yoo, J., & Ju, D. Y. (2014). Data preloading technique using intention prediction. In Human-Computer Interaction: Applications and Services - 16th International Conference, HCI International 2014, Proceedings (PART 3 ed., pp. 32-41). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8512 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-319-07227-2_4
Lee, Seungyup ; Yoo, Juwan ; Ju, Da Young. / Data preloading technique using intention prediction. Human-Computer Interaction: Applications and Services - 16th International Conference, HCI International 2014, Proceedings. PART 3. ed. Springer Verlag, 2014. pp. 32-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).
@inproceedings{b2db298d8985487fa91bb6d083f681bb,
title = "Data preloading technique using intention prediction",
abstract = "Various smart devices provide fast response time and ubiquitous web-environment to users for better user experiences (UXs). However, high device performance that users perceive is not always promised because there should be limited network bandwidth, and computation capabilities. When the network and computation capabilities are overloaded, users experience buffering and loading time to accomplish a certain task. We, therefore, propose data preloading technique [1], which predicts user intention and preloads the web and local application data to provide better device performance in spite of poor network conditions and outdated hardware. We also design intention cognitive model to predict user intention precisely. Four user intention prediction algorithms, which are applicable to various conventional input methods, are described and compared each performance in both user's and device's aspects.",
author = "Seungyup Lee and Juwan Yoo and Ju, {Da Young}",
year = "2014",
month = "1",
day = "1",
doi = "10.1007/978-3-319-07227-2_4",
language = "English",
isbn = "9783319072265",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 3",
pages = "32--41",
booktitle = "Human-Computer Interaction",
address = "Germany",
edition = "PART 3",

}

Lee, S, Yoo, J & Ju, DY 2014, Data preloading technique using intention prediction. in Human-Computer Interaction: Applications and Services - 16th International Conference, HCI International 2014, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 8512 LNCS, Springer Verlag, pp. 32-41, 16th International Conference on Human-Computer Interaction: Applications and Services, HCI International 2014, Heraklion, Crete, Greece, 14/6/22. https://doi.org/10.1007/978-3-319-07227-2_4

Data preloading technique using intention prediction. / Lee, Seungyup; Yoo, Juwan; Ju, Da Young.

Human-Computer Interaction: Applications and Services - 16th International Conference, HCI International 2014, Proceedings. PART 3. ed. Springer Verlag, 2014. p. 32-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8512 LNCS, No. PART 3).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Data preloading technique using intention prediction

AU - Lee, Seungyup

AU - Yoo, Juwan

AU - Ju, Da Young

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Various smart devices provide fast response time and ubiquitous web-environment to users for better user experiences (UXs). However, high device performance that users perceive is not always promised because there should be limited network bandwidth, and computation capabilities. When the network and computation capabilities are overloaded, users experience buffering and loading time to accomplish a certain task. We, therefore, propose data preloading technique [1], which predicts user intention and preloads the web and local application data to provide better device performance in spite of poor network conditions and outdated hardware. We also design intention cognitive model to predict user intention precisely. Four user intention prediction algorithms, which are applicable to various conventional input methods, are described and compared each performance in both user's and device's aspects.

AB - Various smart devices provide fast response time and ubiquitous web-environment to users for better user experiences (UXs). However, high device performance that users perceive is not always promised because there should be limited network bandwidth, and computation capabilities. When the network and computation capabilities are overloaded, users experience buffering and loading time to accomplish a certain task. We, therefore, propose data preloading technique [1], which predicts user intention and preloads the web and local application data to provide better device performance in spite of poor network conditions and outdated hardware. We also design intention cognitive model to predict user intention precisely. Four user intention prediction algorithms, which are applicable to various conventional input methods, are described and compared each performance in both user's and device's aspects.

UR - http://www.scopus.com/inward/record.url?scp=84903178448&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84903178448&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-07227-2_4

DO - 10.1007/978-3-319-07227-2_4

M3 - Conference contribution

AN - SCOPUS:84903178448

SN - 9783319072265

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 32

EP - 41

BT - Human-Computer Interaction

PB - Springer Verlag

ER -

Lee S, Yoo J, Ju DY. Data preloading technique using intention prediction. In Human-Computer Interaction: Applications and Services - 16th International Conference, HCI International 2014, Proceedings. PART 3 ed. Springer Verlag. 2014. p. 32-41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). https://doi.org/10.1007/978-3-319-07227-2_4