Browsing by Author "Murillo, Angela P."

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    Application of Edge-to-Cloud Methods Toward Deep Learning
    (IEEE, 2022-10) Choudhary, Khushi; Nersisyan, Nona; Lin, Edward; Chandrasekaran, Shobana; Mayani, Rajiv; Pottier, Loic; Murillo, Angela P.; Virdone, Nicole K.; Kee, Kerk; Deelman, Ewa; Library and Information Science, School of Computing and Informatics
    Scientific workflows are important in modern computational science and are a convenient way to represent complex computations, which are often geographically distributed among several computers. In many scientific domains, scientists use sensors (e.g., edge devices) to gather data such as CO2 level or temperature, that are usually sent to a central processing facility (e.g., a cloud). However, these edge devices are often not powerful enough to perform basic computations or machine learning inference computations and thus applications need the power of cloud platforms to generate scientific results. This work explores the execution and deployment of a complex workflow on an edge-to-cloud architecture in a use case of the detection and classification of plankton. In the original application, images were captured by cameras attached to buoys floating in Lake Greifensee (Switzerland). We developed a workflow based on that application. The workflow aims to pre-process images locally on the edge devices (i.e., buoys) then transfer data from each edge device to a cloud platform. Here, we developed a Pegasus workflow that runs using HTCondor and leveraged the Chameleon cloud platform and its recent CHI@Edge feature to mimic such deployment and study its feasibility in terms of performance and deployment.
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    Blueprint: Cyberinfrastructure Center of Excellence
    (Zenodo, 2021-03) Deelman, Ewa; Mandal, Anirban; Murillo, Angela P.; Nabrzyski, Jarek; Pascucci, Valerio; Ricci, Robert; Baldin, Ilya; Sons, Susan; Christopherson, Laura; Vardeman, Charles; Ferreira da Silva, Rafael; Wyngaard, Jane; Petruzza, Steve; Rynge, Mats; Vahi, Karan; Whitcup, Wendy R.; Drake, Josh; Scott, Erik; Library and Information Science, School of Informatics and Computing
    In 2018, NSF funded an effort to pilot a Cyberinfrastructure Center of Excellence (CI CoE or Center) that would serve the cyberinfrastructure (CI) needs of the NSF Major Facilities (MFs) and large projects with advanced CI architectures. The goal of the CI CoE Pilot project (Pilot) effort was to develop a model and a blueprint for such a CoE by engaging with the MFs, understanding their CI needs, understanding the contributions the MFs are making to the CI community, and exploring opportunities for building a broader CI community. This document summarizes the results of community engagements conducted during the first two years of the project and describes the identified CI needs of the MFs. To better understand MFs' CI, the Pilot has developed and validated a model of the MF data lifecycle that follows the data generation and management within a facility and gained an understanding of how this model captures the fundamental stages that the facilities' data passes through from the scientific instruments to the principal investigators and their teams, to the broader collaborations and the public. The Pilot also aimed to understand what CI workforce development challenges the MFs face while designing, constructing, and operating their CI and what solutions they are exploring and adopting within their projects. Based on the needs of the MFs in the data lifecycle and workforce development areas, this document outlines a blueprint for a CI CoE that will learn about and share the CI solutions designed, developed, and/or adopted by the MFs, provide expertise to the largest NSF projects with advanced and complex CI architectures, and foster a community of CI practitioners and researchers.
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    Broadening student engagement to build the next generation of cyberinfrastructure professionals
    (Association for Computing Machinery, 2023) Murillo, Angela P.; Brower, Don; Hossain, Sarowar; Kee, Kerk; Mandel, Anirban; Nabrzyski, Jarek; Scott, Erik; Ewing, Rodney; Deelman, Ewa
    The CI Compass Fellowship Program (CICF) was developed to broaden undergraduate student participation in cyberinfrastructure (CI) research, development, and operations. CICF is a distinctive program for undergraduate students pursuing studies in computer science, information science, data science, and other related fields. During year one of the program, CICF had six students participate from two institutions. During year 2 of the program, CICF had fourteen students participate from nine institutions. This poster provides details of the CICF program development and summarizes the impact of the first two years.
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    Broadening Student Engagement To Build the Next Generation of Cyberinfrastructure Professionals
    (ACM, 2023-07) Murillo, Angela P.; Brower, Don; Hossain, Sarowar; Kee, Kerk; Mandal, Anirban; Nabrzyski, Jarek; Scott, Erik; Virdone, Nicole; Ewing, Rodney; Deelman, Ewa; Library and Information Science, Luddy School of Informatics, Computing, and Engineering
    The CI Compass Fellowship Program (CICF) was developed to broaden undergraduate student participation in cyberinfrastructure (CI) research, development, and operations. CICF is a distinctive program for undergraduate students pursuing studies in computer science, information science, data science, and other related fields. During year one of the program, CICF had six students participate from two institutions. During year 2 of the program, CICF had fourteen students participate from nine institutions. This poster provides details of the CICF program development and summarizes the impact of the first two years.
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    Broadening Student Participation in Cyberinfrastructure Research and Development
    (Association for Computing Machinery, 2022) Murillo, Angela P.; Deelman, Ewa; Nabrzyski, Jarek; Pottier, Loïc
    This poster presents preliminary observations from the pilot year of a CI Compass Fellowship Program (CICF) that was created to broaden student participation in cyberinfrastructure research and development. CICF is part of the CI Compass project, which is the National Science Foundation (NSF) Cyberinfrastructure Center of Excellence, created to provide support and enhance the data lifecycle of NSF Major Facilities (MFs) [1]. MFs are the largest-scale scientific efforts that the NSF supports and are highly diverse, have heterogeneous data, and a wide range of cyberinfrastructure for capturing, processing, archiving, and disseminating data, as well as providing access to sophisticated instruments and computational capabilities. MFs span many science domains, including astronomy, climate, ecology, natural hazard, ocean science, physics, and seismology [2]. Due to the complexity of the cyberinfrastructure and data that supports MFs, it is critical that we create educational opportunities for students interested in pursuing a career in this specialized cyberinfrastructure that supports large-scale science. The CICF program aims to provide students the opportunity to learn about cyberinfrastructure development and MFs, develop cyberinfrastructure-related skill sets important to the work of MFs, and engage directly with the MF CI professionals.
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    Coming to America: Iranians' use of Telegram for immigration information seeking
    (Emerald, 2020) Nikkhah, Sarah; Murillo, Angela P.; Young, Alyson Leigh; Miller, Andrew D.; Human-Centered Computing, School of Informatics and Computing
    Purpose This study examines Iran-US migrants' use of the most popular messaging application in Iran—Telegram—and shows how they use it to manage their migration information practices. Design/methodology/approach This study took a qualitative observation approach. Over the course of six months, over 80 h of observations were conducted on Iran-US migration-related settings within Telegram. Findings This work identifies the information practices that emerge as users seek and share information related to Iran-US migration. Telegram plays a vital role across the immigration stages, predominantly in the pre-migration stage. This work also shows how the constraints and features of Telegram influence users' information sharing and seeking practices. Practical implications The findings support the implication that a social media platform that provides multiple ways to interact is likely to better support niche or unanticipated uses. Originality/value This study is the first of its kind to explore Iranian Immigrants information practices in the US. The immigration information practices observed during this study represent a valuable example of end-user appropriation within extraordinary constraints, which may be of use in other information-seeking contexts where dedicated or bespoke tools are impractical or ill-advised.
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    Community Data Curation Competencies Framework
    (iSchools, 2023-03) Murillo, Angela P.; Yoon, Ayoung; Library and Information Science, Luddy School of Informatics, Computing, and Engineering
    Library and information science (LIS) professionals and educators have spearheaded data curation, providing services in research data management and scientific data curation, and delivering data curation education for the academic workforce. While public and private sector community organiza-tions, such as local government, non-profit organizations, and community-based organizations have become more data-driven, the specific data cura-tion needs of these organizations are not fully addressed in current data cura-tion research or education. This research aims to bridge this gap in existing data curation education by creating a community data curation competencies framework, developing a pilot curriculum based on this framework, and evaluating and disseminating the developed curriculum. This poster presents the preliminary results of the first phase of this project, where we developed a protocol to review existing data curation competencies to create the initial framework.
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    Community Data Curation Curriculum Development
    (2023-09) Murillo, Angela P.; Yoon, Ayoung; Library and Information Science, Luddy School of Informatics, Computing, and Engineering
    This poster presents the preliminary findings of the two-year project, Community Data Curation Competency, which aims to extend the current understanding of data curation competencies in the context of community data, engage with strategic stakeholders (educators, practitioners, community organizations) in the process of curriculum design, and design a data curation pilot curriculum to fulfill the current gaps in data curation education to include community data curation competencies.
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    Confronting the Challenges of Computational and Social Perspectives of the Data Continuum
    (Sciendo, 2020-06) Murillo, Angela P.; Curty, Renata G.; Jeng, Wei; He, Daqing; Library and Information Science, School of Informatics and Computing
    As the availability of data is increasing everyday, the need to reflect on how to make these data meaningful and impactful becomes vital. Current data paradigms have provided data life cycles that often focus on data acumen and data stewardship approaches. In an effort to examine the convergence, tensions, and harmonies of these two approaches, a group of researchers participated in an interactive panel session at the Association of Information Science and Technology Annual meeting in 2019. The panel presenters described their various research activities in which they confront the challenges of the computational and social perspectives of the data continuum. This paper provides a summary of this interactive panel.
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    The Convergence of Computational and Social Approaches for Unveiling Meaningful and Valuable Data
    (Wiley, 2019) Murillo, Angela P.; Curty, Renata G.; Jeng, Wei; He, Daqing; Library and Information Science, School of Informatics and Computing
    The current data paradigm is seeking a more integrated and comprehensive framework to make sense of data and its derived issues. From the perspective of the data life cycle, we argue that computational and social approaches complement each other to confront data challenges. Computational approaches consist of ETL (extract, transform, and load), modeling, and machine learning techniques; social approaches include policy and regulations, data sharing and reuse behavior, reproducibility, ethical and privacy issues. In this panel, we frame these two approaches as data acumen and data stewardship. The merging of these two perspectives allows data not only to become discoverable, accessible, and interoperable, but also to further the value of revealing meaningful patterns and become supportive evidence for important decision making. In this panel, the opening facilitator and three panelists will report on their recent studies in terms of this convergence of both data acumen and stewardship while sharing their recent research insights on case studies in three disciplines: agriculture, biomedicine, and archeology.