DOCSLIB.ORG

An Interview with Yolanda Gil

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
An Interview with Yolanda Gil Successful Research in AI Reflections on Successful Research in Artificial Intelligence: An Interview with Yolanda Gil Yolanda Gil, Biplav Srivastava, Ching-Hua Chen, Oshani Seneviratne This article contains the observations ditorial Team: What are your observations on how has of Yolanda Gil, director of knowledge the nature of AI research changed and evolved over technologies and research professor at the years? the Information Sciences Institute of E Gil: I worked on my thesis in the 1980s, when AI research the University of Southern California, methodology was very different than it is today. Back then, USA, and president of Association for AI research was very broad and cross-disciplinary. The Advancement of Artificial Intelligence who was recently interviewed about machine learning conferences I attended would include the factors that could influence suc- cognitive psychologists developing new capabilities for edu- cessful AI research. The editorial team cation and learning, human computer interaction researchers interviewers included members of the doing work on knowledge acquisition, and mathematicians special track editorial team from IBM focused on the statistics of machine learning and formal Research (Biplav Srivastava, Ching-Hua aspects of learning. Because of this, AI researchers like Chen) and Rensselaer Polytechnic Insti- myself would naturally mingle with researchers from other tute (Oshani Seneviratne). disciplines. In contrast, today we see research that is more narrow and focused. This could be a sign of a maturing field, because sometimes when a field grows people tend to spe- cialize into very focused areas. But having a narrow view of AI is not necessarily a good thing in my opinion. 6 AI MAGAZINE Copyright © 2019, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 Successful Research in AI Editorial Team: How does one evaluate AI research? sustain basic research. The Internet came out of gov- Gil: For me, the evaluation of research in AI de- ernment investment, and so did technologies like chat pends on the disciplinary angle that is taken. There bots and search engines, all of which are pervasive in are significant differences in evaluation and in meth- our society today. So, we are reaping the benefits of odology for AI research that focus on human com- investments in basic research that were made by the puter interaction, or cognitive science, or physics, government decades ago. Sometimes government or social networking, or philosophy. For example, a funding agencies, perhaps influenced by the inter- cognitive psychology methodology may place a lot ests of the commercial sector in funding the next of emphasis on theoretical models of the mind and start-up, seem very focused on projects that have im- implementing AI systems that approximate them mediate applications. I think it’s really important for well based on particular measurements from user the government to retain a focus on long-term prob- studies. In contrast, a more mathematical method- lems and to consider sustained decadal-scale funding ology may evaluate accuracy based on some statis- programs for the most challenging AI topics. tical metric and may be more focused on algorith- Editorial Team: What skills are essential for young mic improvement. So different evaluation methods AI researchers to have? are appropriate for different kinds of AI research. Gil: I teach my students to be very mindful of long- Editorial Team: Is it difficult to learn how to under- term, deep ideas. Students will often focus on the next stand and appreciate different disciplinary angles? paper, or on the things they can do today to extend Gil: You have to learn enough about the other dis- some code they are working on. However, one of the cipline, which can be a challenge. I’ll let you in on a most important skills for any scientist to have is to little secret: Long plane rides are a great opportunity think deeply about a very challenging problem. To do to learn something new and different. Whenever I that, you need to invest the time. You need to make am on one, I really cherish the opportunity to read a commitment to spend time defining and thinking without interruption. Before the trip, I will prepare about that problem. It is much harder to think about a big folder of materials and books to teach myself what will be important in 10 years than it is to think about a topic that I am less familiar with. Reading about the next paper or the next line of code. It is very them makes the trip feel very short and I can learn hard to devote yourself to thinking rather than cod- something about a new discipline or topic that I have ing, but it is very important if we want fundamental been curious about! advances in AI. For students, this is especially hard, Editorial Team: What factors do you see inhibiting and as professors, we need to teach it. the rate of success in AI research? This is also why it is important for students to go to Gil: We need to emphasize more the importance a broad conference like Association for the Advance- of reproducibility, which is a fundamental aspect of ment of Artificial Intelligence (AAAI), to get a good the scientific method. One key component of repro- view of different research topics in AI, which can ducibility is sharing and publishing (with DOIs) all promote deeper thinking about problems. For exam- of your data and your code together with execution ple, at the AAAI conference, you will find sessions on details (such as parameters and key intermediate re- machine learning and natural language processing, sults). This is important because it helps document the others on robotics and human computer interaction, specific work that was done. It helps other researchers and others on reasoning and planning. And many of understand the context of the claims you are making. the papers will combine research on several areas of It also helps others build on what you did and vice AI. You will find both interesting applications and versa. This is something I have been promoting and fundamental research. I also attend specialized con- pushing in the scientific-paper-of-the future initiative.1 ferences, but I find the AAAI conference most inspir- This initiative promotes a future in which all relevant ing for formulating long-term research problems. details of a computational experiment are exposed Finally, a problem I find that it is when I am I think- properly and in a structured way. ing about long-term topics that I am most excited, A positive trend that we are seeing now is that more work the hardest, and get the most satisfaction at a and more people want to share their results in the open personal level. as soon as possible. The cycle of waiting for six months Editorial Team: Because deeper ideas take time to to see your paper come out is too long. People are pub- develop, and publications may not come about so lishing their work on ArXiv so that their results can be quickly, what are some methods you use to know out and then they move on to the next idea. There that you are making progress against deeper ideas? is an incredible eagerness by young researchers to dis- Gil: This is how I measure progress: I start a folder seminate their work through public sites. We need to on a far-fetched topic and watch how it grows over encourage this and do it for data and software as well, time. Initially the folder is very thin, but I keep adding for every research product in addition to papers. to it as I think more about the topic. Six months Editorial Team: Many countries have come up with a later it has expanded with details, and in yet an- national AI policy. What are your thoughts on govern- other 6 months it will have expanded further, and ment involvement in AI research and development? so on. At some point, I will have enough of an idea Gil: The government can play an incredibly im- to work with a student, and eventually, I’ll under- portant role. Through its funding programs it can stand the problem enough to formulate a 5-year WINTER 2019 7 Successful Research in AI plan. This is a many-month process, and on day 1 area where I think we should do more: Our AI systems you don’t see how you could possibly measure im- have no notion of their own limitations. They don’t provements, but eventually something clicks, and know what their capabilities are or what exists beyond you can really see the path forward. their capability. In the human world, if you walk into Editorial Team: Many companies are now inter- a pharmacy and ask someone for a flu shot, if that ested in commercializing AI and creating their own person is a cashier or a receptionist, they will tell you narratives around AI. What are your thoughts about why they can’t give you one and they will direct you what you see? on who to go to instead. Our AI systems are rarely Gil: I have been doing AI research for more than 30 designed to do this. When asked a question, our AI years, and I have always seen companies presenting systems will give you an answer, but they don’t know AI in a way that promotes their commercial interests. the context, and they often don’t know the risks in- This is natural and is nothing new. In the 1980s, all volved. I wrote a paper recently about thoughtful AI2 the banks wanted investors and customers to know that talks about how an AI system could have more that they were using expert systems and they had awareness of how it fits into the world so that it could AI capabilities in-house.
Load more

Recommended publications
  • Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models
    Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models Yolanda Gil Information Sciences Institute University of Southern California [email protected] http://www.isi.edu/~gil USC Information Sciences Institute Yolanda Gil 1 Knowledge Technologies at ISI: From Basic Research to Transition AFOSR DARPA DOD 2000 2005 TRELLIS: Basic JIST: Capturing decisions Transition to IC’s research on information and evidence in RDEC experimental analysis capture intelligence analysis platform NSF AFRL DOD 2002 2008 Semantic workflows for Windward: Scalable Transition to IC’s large-scale scientific data Knowledge Discovery RDEC experimental analysis through Distributed Analysis platform AFOSR W3C 2005 2010 MathTrust: Basic research Provenance Incubator on social trust of open leading to standards for information sources provenance on the web AFOSR 2011 Mission - Centered USC InformationNetwork Sciences Models Institute Yolanda Gil 2 http://www.w3.org/2005/Incubator/prov/wiki W3C Provenance Standard Need standard representation for origins of information • Trust in open systems (Web), computational science, process validation and compliance W3C Provenance Incubator launched in 2009 (Y. Gil, Chair) • Collected use cases for provenance • Developed provenance requirements for the Web • Created mappings for existing provenance vocabularies • State-of-the-art report • Provenance in Web architecture Final report in Dec 2012 proposed charter for a Working Group on provenance WG started 4/2011 to develop PROV
    [Show full text]
  • A 20-Year Community Roadmap for Artificial Intelligence Research in the US
    A 20-Year Community Roadmap for Begin forwarded message: From: AAAI Press <[email protected]> Subject: Recap Date: July 8, 2019 at 3:52:40 PM PDT To: Carol McKenna Hamilton <[email protected]>Artificial Intelligence Research in the US Could you check this for me? Hi Denise, While it is fresh in my mind, I wanted to provide you with a recap on the AAAI proceedings work done. Some sections of the proceedings were better than others, which is to be expected because authors in some subject areas are less likely to use gures, the use of which is a signicant factor in error rate. The vision section was an excellent example of this variability. The number of problems in this section was quite high. Although I only called out 13 papers for additional corrections, there were many problems that I simply let go. What continues to be an issue, is why the team didn’t catch many of these errors before compilation began. For example, some of the errors involved the use of the caption package, as well as the existence of setlength commands. These are errors that should have been discovered and corrected before compilation. As I’ve mentioned before, running search routines on the source of all the papers in a given section saves a great deal of time because, in part, it insures consistency. That is something that has been decidedly lacking with regard to how these papers have been (or haven’t been) corrected. It is a cause for concern because the error rate should have been improving, not getting worse.
    [Show full text]
  • Annotated Table of Contents V B I P
    AI MATTERS, VOLUME 1, ISSUE 1!AUGUST 2014 AI Matters Annotated Table of Contents Welcome to AI Matters ! Kiri Wagstaff, Editor Full article: http://doi.acm.org/10.1145/2639475.2639476 A welcome from the Editor of AI Matters and an en- couragement to submit for the next issue. Artificial Intelligence: No Longer Just for O You and Me Yolanda Gil, SIGAI Chair Full article: http://doi.acm.org/10.1145/2639475.2639477 The Chair of SIGAI waxes enthusiastic about the cur- Drop-in Challenge games at RoboCup. rent state of and future prospects for AI developments V Peter Stone, Patrick MacAlpine, Katie Genter, and innovations. She also reports on high school and Sam Barrett. Full image and details: student projects featured at the 2014 Intel Science http://doi.acm.org/10.1145/2639475.2655756 and Engineering Fair. Using Agent-Based Modeling and Cul- Announcing the SIGAI Career Network I N and Conference tural Algorithms to Predict the Location Sanmay Das, Susan L. Epstein, and Yolanda of Submerged Ancient Occupational Gil Sites Full article: http://doi.acm.org/10.1145/2639475.2649581 Robert G. Reynolds, Areej Salaymeh, John O'Shea, and Ashley Lemke SIGAI has created a career networking website and Full article: http://doi.acm.org/10.1145/2639475.2639479 annual conference for the benefit of early career sci- entists. Benefits include mentoring, networking, and A collaboration between archaeologists and artificial job connections. intelligence experts has discovered ancient hunting sites submerged in over 120 feet of water in Lake Future Progress in Artificial Intelligence: Huron. This is the oldest known hunting ground in the world.
    [Show full text]
  • AAAI Members Elect New President-Elect & Executive
    For press inquiries only, contact: Sara Hedberg Emergent, Inc. (425) 444-7272 [email protected] FOR IMMEDIATE RELEASE AAAI members elect new President-Elect & Executive Councilors MENLO PARK, CA. – July 13, 2005. The American Association for Artificial Intelligence (AAAI), the nation’s scientific AI society, announces the new President-Elect and Executive Councilors elected by the society’s membership. Eric Horvitz of Microsoft Research has been elected AAAI President-Elect and will become President in 2007 for a two-year term. Horvitz joins President Alan MacWorth, University of British Columbia, and Ron Brachman, immediate Past President, on the presidential team. Tom Mitchell of Carnegie Mellon University completes his six years of service to AAAI. The newly elected Councilors include: Maria Gini, University of Minnesota Kevin Knight, USC/Information Sciences Institute Peter Stone, University of Texas at Austin Sebastian Thrun, Stanford University Councilors serve a three year term, and one-third of the council rotates off each year. Other Council members include: (through 2006): Steve Chien, JPL / California Institute of Technology Yolanda Gil, USC / ISI Haym Hirsh, University of Rutgers Andrew Moore, Carnegie Mellon University (through 2007): Oren Etzioni, University of Washington Lise Getoor, University of Maryland, College Park Karen Myers, SRI International Illah Nourbakhsh, NASA Ames Research Center / Carnegie Mellon University Councilors who complete their three years of service this year include: Carla Gomes, Cornell University Michael Littman, Rutgers University Maja Mataric, University of Southern California Yoav Shoham, Stanford University (more) “We are very grateful to Tom Mitchell and to these retiring Councilors who have given – and continue to give -- so generously of their time and talents to the organization,” says Carol Hamilton, AAAI Executive Director.
    [Show full text]
  • Ricky J. Sethi Curriculum Vitae 2020
    Ricky J. Sethi Curriculum Vitae 2020 CONTACT Fitchburg State University w: research.sethi.org 160 Pearl Street e: [email protected] Fitchburg, MA 01420 p: 978.665.3703 RESEARCH My research uses fundamental ideas from machine learning and computational science INTERESTS in social computing (fact-checking misinformation and virtual communities), data sci- ence (semantic workflows for digital humanities and reproducibility), and computer vision (physics-based methods for group and crowd analysis). EDUCATION Ph.D., Computer Science . 2004 - 2009 • University of California, Riverside Adviser: Amit K. Roy-Chowdhury Committee: Eamonn J. Keogh and Christian R. Shelton Area of Study: Artificial Intelligence/Computer Vision M.S., Physics/Business (Information Systems) . 1999 - 2001 • University of Southern California B.A., Molecular and Cellular Biology, Neurobiology (Physics minor) . 1996 • University of California, Berkeley ACADEMIC APPOINTMENTS Associate Professor of Computer Science . 2014 - Present • Associate Professor, 2018 - Present Assistant Professor, 2014 - 2018 Fitchburg State University Director of Research . 2013 - Present • The Madsci Network Consulting Scientist . 2018 - Present • US National Academy of Sciences (NAS)’s The Science and Entertainment Exchange Team Lead and Adjunct Professor . 2013 - Present • Southern New Hampshire University Research Scientist. .2014 • Postdoctoral Associate UMass Amherst/UMass Medical School NSF Computing Innovation Fellow . 2010 - 2013 • University of California, Los Angeles University of Southern
    [Show full text]
  • Towards the Geoscience Paper of the Future: Best Practices for Documenting and Sharing Research from Data to Software to Provenance
    Accepted for publication in the Earth and Space Science Journal, 2016. Towards the Geoscience Paper of the Future: Best Practices for Documenting and Sharing Research from Data to Software to Provenance Yolanda Gil1 (orcid.org/0000-0001-8465-8341) Information Sciences Institute and Department of Computer Science, University of Southern California Cédric H. David (orcid.org/0000-0002-0924-5907) Jet Propulsion Laboratory, California Institute of Technology Ibrahim Demir (orcid.org/0000-0002-0461-1242) IIHR Hydroscience & Engineering Institute, University of Iowa Bakinam T. Essawy (orcid.org/0000-0003-2295-7981) Department of Civil and Environmental Engineering, University of Virginia Robinson W. Fulweiler (orcid.org/0000-0003-0871-4246) Department of Earth and Environment, Department of Biology, Boston University Jonathan L. Goodall (orcid.org/0000-0002-1112-4522) Department of Civil and Environmental Engineering, University of Virginia Leif Karlstrom (orcid.org/0000-0002-2197-2349) Department of Geological Sciences, University of Oregon, Eugene, Oregon, 97403 Huikyo Lee (orcid.org/0000-0003-3754-3204) Jet Propulsion Laboratory, California Institute of Technology Heath J. Mills (orcid.org/0000-0002-2882-9238) Division of Natural Sciences, University of Houston Clear Lake Ji-Hyun Oh (orcid.org/0000-0001-8989-7691) Computer Science Department of the University of Southern California & Jet Propulsion Laboratory, California Institute of Technology Suzanne A Pierce (orcid.org/0000-0002-3050-1987) Texas Advanced Computing Center and Jackson School of Geosciences, University of Texas at Austin Allen Pope (orcid.org/0000-0001-9699-7500) National Snow and Ice Data Center, University of Colorado Boulder & Polar Science Center, Applied Physics Lab, University of Washington Mimi W.
    [Show full text]
  • Arxiv:1705.02607V2 [Cs.SE] 18 May 2017 0002-3444-7615
    REPORT ON THE FOURTH WORKSHOP ON SUSTAINABLE SOFTWARE FOR SCIENCE: PRACTICE AND EXPERIENCES (WSSSPE4) DANIEL S. KATZ(1), KYLE E. NIEMEYER(2), SANDRA GESING(3), LORRAINE HWANG(4), WOLFGANG BANGERTH(5), SIMON HETTRICK(6), RAY IDASZAK(7), JEAN SALAC(8), NEIL CHUE HONG(9), SANTIAGO NÚÑEZ-CORRALES(10), ALICE ALLEN(11), R. STUART GEIGER(12), JONAH MILLER(13), EMILY CHEN(14), ANSHU DUBEY(15), PATRICIA LAGO(16) Abstract. This report records and discusses the Fourth Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE4). The report includes a description of the keynote presentation of the workshop, the mission and vision statements that were drafted at the workshop and finalized shortly after it, a set of idea papers, position papers, experience papers, demos, and lightning talks, and a panel discussion. The main part of the report covers the set of working groups that formed during the meeting, and for each, discusses the participants, the objective and goal, and how the objective can be reached, along with contact information for readers who may want to join the group. Finally, we present results from a survey of the workshop attendees. (1) National Center for Supercomputing Applications (NCSA) & Department of Computer Science & Department of Electrical and Computer Engineering & School of Information Sciences (iSchool), University of Illinois, Urbana– Champaign, IL, USA; [email protected]; ORCID: 0000-0001-5934-7525. (2) School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, Corvallis, OR, USA; [email protected]; ORCID: 0000-0003-4425-7097. (3) Center for Research Computing & Department of Computer Science and Engineering, University of Notre Dame, IN, USA; [email protected]; ORCID: 0000-0002-6051-0673.
    [Show full text]
  • The Transformative Potential of AI for Science
    The Transforma,ve Poten,al of AI for Science: Will AI Write the Scien,fic Papers of the Future? Yolanda Gil Informaon Sciences Ins.tute and Department of Computer Science University of Southern California h<ps://.nyurl.com/yolandagil-aaai2020 This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as the original source is credited. Source: “Will AI Write the Scien.fic Papers of the Future?”, Yolanda Gil, 2020 Presiden.al Address, Associaon for the Advancement of Ar.ficial Intelligence (AAAI), February 2020. A Personal Perspecve on AI A Personal Perspecve on AI • The AI community has always been • Visionary • Broad • Inclusive • Interdisciplinary • Determined • And dare I say • Successful A Personal View of Watershed Moments in AI: (I) 1980s Scripts Plans Goals and Understanding Roger C. Schank and Robert P. Abelson A Personal View of Watershed Moments in AI: 1990s 1990: Sphynx shows speaker independent large vocabulary connuous speech recognion – used it to write my PhD thesis 1992: Soar flies helicopter teams in simulaons and is indisnguishable to commanders from human-controlled aircra 1992: TD-Gammon autonomously learns to play backgammon at human player levels 1995: SKICAT idenfies five new quasars in the Second Palomar Sky Survey 1995: Navlab is the first trained car to drive autonomously on highways to cross the United States 1997: Deep Blue defeats human chess world champion and gets grandmaster-level rang 1999: RAX flew a spacecraC autonomously, demonstrang planning, monitoring, and fault
    [Show full text]
  • Prodigy Bidirectional Planning
    Prodigy Bidirectional Planning Eugene Fink Jim Blythe Computer Science Information Sciences Institute Carnegie Mellon University University of Southern California Pittsburgh, pa 15213 Marina del Rey, ca 90292 e.fi[email protected] [email protected] www.cs.cmu.edu/∼eugene www.isi.edu/∼blythe Abstract The prodigy system is based on bidirectional planning, which is a combination of goal-directed reasoning with simulated execution. Researchers have implemented a se- ries of planners that utilize this search strategy, and demonstrated that it is an efficient technique, a fair match for other successful planners; however, they have provided few formal results on the common principles underlying the developed algorithms. We formalize bidirectional planning, elucidate some techniques for improving its ef- ficiency, and show how different strategies for controlling search complexity give rise to different versions of prodigy. In particular, we demonstrate that prodigy is incom- plete and discuss advantages and drawbacks of its incompleteness. We then develop a complete bidirectional planner and compare it experimentally with prodigy.We show that it is almost as fast as prodigy and solves a wider range of problems. 1 Introduction Newell and Simon [1961; 1972] invented means-ends analysis during their work on the General Problem Solver (gps), back in the early days of artificial intelligence. Their technique combined goal-directed reasoning with forward chaining from the initial state. The authors of later planning systems [Fikes and Nilsson, 1971; Warren, 1974; Tate, 1977] gradually abandoned forward search and began to rely exclusively on backward chaining. Researchers studied several types of backward chainers [Minton et al., 1994] and showed that least commitment improves the efficiency of goal-directed reasoning, which gave rise to tweak [Chapman, 1987], abtweak [Yang et al., 1996], snlp [McAllester and Rosenblitt, 1991], ucpop [Penberthy and Weld, 1992; Weld, 1994], and other least-commitment planners.
    [Show full text]
  • AI MAGAZINE AAAI News
    AAAI News AAAI News Spring News from the Association for the Advancement of Artificial Intelligence AAAI Announces New Senior Members! 2017 Feigenbaum Prize Awarded! AAAI congratulates the following indi - AAAI is delighted to announce that Yoav Shoham (Stanford viduals on their election to AAAI Senior University, Google), has been selected as the recipient of the Member status: 2017 AAAI Feigenbaum Prize. Shoham is being recognized in Alessandro Cimatti (Fondazione Bruno particular for high-impact basic research in artificial intelli - Kessler, Italy) gence — including knowledge representation, multiagent sys - tems, and computational game theory — and translating the basic research Xuelong Li (Chinese Academy of Sci - into impactful and innovative commercial products. ences, China) The AAAI Feigenbaum Prize was established in 2010 and is awarded bien - Nathan R. Sturtevant (University of nially to recognize and encourage outstanding Artificial Intelligence Denver, USA) research advances that are made by using experimental methods of com - This honor was announced at the puter science. The associated cash prize of $10,000 is provided by the recent AAAI-17 Conference in San Feigenbaum Nii Foundation. Francisco. Senior Member status is designed to recognize AAAI members who have achieved significant accom - plishments within the field of artificial cial Intelligence, held in 1999 in Orlan - heads the UW Robotics and State Esti - intelligence. To be eligible for nomina - do, Florida, USA. The 2017 recipient of mation Lab (RSE-Lab). Fox is a Fellow tion for Senior Member, candidates the AAAI Classic Paper Award was: of the AAAI and IEEE, and he received must be consecutive members of AAAI Monte Carlo Localization: Efficient several best paper awards at major for at least five years and have been Position Estimation for Mobile Robots.
    [Show full text]
  • The Open Provenance Model Core Specification (V1.1)
    Future Generation Computer Systems 27 (2011) 743–756 Contents lists available at ScienceDirect Future Generation Computer Systems journal homepage: www.elsevier.com/locate/fgcs The Open Provenance Model core specification (v1.1) Luc Moreau a,∗, Ben Clifford, Juliana Freire b, Joe Futrelle c, Yolanda Gil d, Paul Groth e, Natalia Kwasnikowska f, Simon Miles g, Paolo Missier h, Jim Myers c, Beth Plale i, Yogesh Simmhan j, Eric Stephan k, Jan Van den Bussche f a U. of Southampton, United Kingdom b U. Utah, United States c National Center for Supercomputing Applications, United States d Information Sciences Institute, USC, United States e VU University Amsterdam, The Netherlands f U. Hasselt and Transnational U. Limburg, Belgium g King's College, London, United Kingdom h U. of Manchester, United Kingdom i Indiana University, United States j Microsoft, United States k Pacific Northwest National Laboratory, United States article info a b s t r a c t Article history: The Open Provenance Model is a model of provenance that is designed to meet the following Received 21 December 2009 requirements: (1) Allow provenance information to be exchanged between systems, by means of a Received in revised form compatibility layer based on a shared provenance model. (2) Allow developers to build and share tools that 14 June 2010 operate on such a provenance model. (3) Define provenance in a precise, technology-agnostic manner. (4) Accepted 10 July 2010 Support a digital representation of provenance for any ``thing'', whether produced by computer systems or Available online 16 July 2010 not. (5) Allow multiple levels of description to coexist.
    [Show full text]
  • Mining Abstractions in Scientific Workflows
    Departamento de Inteligencia Artificial Escuela T´ecnicaSuperior de Ingenieros Inform´aticos PhD Thesis Mining Abstractions in Scientific Workflows Author: Daniel Garijo Verdejo Supervisors: Prof. Dr. Oscar Corcho Prof. Dra. Yolanda Gil December, 2015 ii Tribunal nombrado por el Sr. Rector Magfco. de la Universidad Polit´ecnicade Madrid, el d´ıa30 de octubre de 2015 Presidente: Dra. Asunci´onG´omezP´erez Vocal: Dr. Jose Manuel G´omezP´erez Vocal: Dr. Malcolm Atkinson Vocal: Dr. Rafael Tolosana Secretario: Dr. Mark Wilkinson Suplente: Dr. Mariano Fern´andezL´opez Suplente: Dra. Bel´enD´ıaz Agudo Realizado el acto de defensa y lectura de la Tesis el d´ıa3 de diciembre de 2015 en la Facultad de Inform´atica Calificac´ıon: EL PRESIDENTE VOCAL 1 VOCAL 2 VOCAL 3 EL SECRETARIO iii iv A mis padres v vi Acknowledgements Finally, after five years, I can finally say that I see light at the end of the tunnel. Maybe the other side is still a bit cloudy at the moment, but the important thing is to have arrived here. And, honestly, I think I wouldn't have made it to this point without all the people who have been by my side during these years. First, I would like to thank my supervisors Oscar Corcho and Yolanda Gil for guiding me whenever I got stuck and for having the patience to answer all my questions. Furthermore, thanks to their help, together with Asunci´on G´omezP´erez'sadvice, I was granted the FPU (Formaci´onde Profesorado Universitario) scholarship from the Ministerio de Ciencia e Innovaci´on.This scholarship has funded the internships and the research described on this document, and I am very grateful for having had the opportunity to enjoy it.
    [Show full text]