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Democratizing Interaction Mining
Democratizing Interaction Mining
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102859
- ISBN
- 9798291578247
- DDC
- 004
- 저자명
- Arsan, Deniz.
- 서명/저자
- Democratizing Interaction Mining
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2023
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2023
- 형태사항
- 93 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Kumar, Ranjitha.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
- 초록/해제
- 요약In the digital landscape, defined by a multitude of mobile applications spanning various platforms, our daily lives are shaped by the quality of digital experiences. The impact of these experiences extends beyond individual satisfaction and directly influences the success of organizations, driving the need for data-driven methods to evaluate and enhance user interfaces. Traditional approaches like analytics and A/B testing, while valuable, require access to an application's codebase, limiting their utility for external applications. In response to these limitations, interaction mining has emerged as a potent technique. Interaction mining entails the capture of design and interaction data as users engage with an application, resulting in the creation of interaction traces. However, existing interaction mining systems rely on intricate OS-level interventions to enable comprehensive data capture.This dissertation introduces On-Device Interaction Mining (odim), a framework that democratizes interaction mining. odim enables data capture by enabling in-the-wild interaction data collection from any Android app on personal devices, without the need for specialized hardware or modifications to the operating system. odim empowers researchers, designers, and industry practitioners to enhance task automation, ensure robust user privacy, and bridge the gap between analytics and UX testing. These contributions provide the tools and methodologies needed to innovate digital experiences while safeguarding user privacy. odim is a step towards a more accessible, principled, and privacy-conscious future for interaction mining.
- 일반주제명
- Computer science
- 일반주제명
- Computer engineering
- 키워드
- Mobile apps
- 키워드
- On-device
- 기타저자
- University of Illinois at Urbana-Champaign Computer Science
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■1001 ▼aArsan, Deniz.
■24510▼aDemocratizing Interaction Mining
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2023
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2023
■300 ▼a93 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Kumar, Ranjitha.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2023.
■520 ▼aIn the digital landscape, defined by a multitude of mobile applications spanning various platforms, our daily lives are shaped by the quality of digital experiences. The impact of these experiences extends beyond individual satisfaction and directly influences the success of organizations, driving the need for data-driven methods to evaluate and enhance user interfaces. Traditional approaches like analytics and A/B testing, while valuable, require access to an application's codebase, limiting their utility for external applications. In response to these limitations, interaction mining has emerged as a potent technique. Interaction mining entails the capture of design and interaction data as users engage with an application, resulting in the creation of interaction traces. However, existing interaction mining systems rely on intricate OS-level interventions to enable comprehensive data capture.This dissertation introduces On-Device Interaction Mining (odim), a framework that democratizes interaction mining. odim enables data capture by enabling in-the-wild interaction data collection from any Android app on personal devices, without the need for specialized hardware or modifications to the operating system. odim empowers researchers, designers, and industry practitioners to enhance task automation, ensure robust user privacy, and bridge the gap between analytics and UX testing. These contributions provide the tools and methodologies needed to innovate digital experiences while safeguarding user privacy. odim is a step towards a more accessible, principled, and privacy-conscious future for interaction mining.
■590 ▼aSchool code: 0090.
■650 4▼aComputer science
■650 4▼aComputer engineering
■653 ▼aInteraction mining
■653 ▼aMobile apps
■653 ▼aOn-device
■653 ▼aDigital landscape
■690 ▼a0984
■690 ▼a0464
■690 ▼a0800
■71020▼aUniversity of Illinois at Urbana-Champaign▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365941▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
