O. Peter Buneman Curriculum Vitæ – Jan 2008
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Are We Losing Our Ability to Think Critically?
news Society | DOI:10.1145/1538788.1538796 Samuel Greengard Are We Losing Our Ability to Think Critically? Computer technology has enhanced lives in countless ways, but some experts believe it might be affecting people’s ability to think deeply. OCIETY HAS LONG cherished technology alters the way we see, hear, the ability to think beyond and assimilate our world—the act of the ordinary. In a world thinking remains decidedly human. where knowledge is revered and innovation equals Rethinking Thinking Sprogress, those able to bring forth Arriving at a clear definition for criti- greater insight and understanding are cal thinking is a bit tricky. Wikipedia destined to make their mark and blaze describes it as “purposeful and reflec- a trail to greater enlightenment. tive judgment about what to believe or “Critical thinking as an attitude is what to do in response to observations, embedded in Western culture. There experience, verbal or written expres- is a belief that argument is the way to sions, or arguments.” Overlay technolo- finding truth,” observes Adrian West, gy and that’s where things get complex. research director at the Edward de For better or worse, exposure to technology “We can do the same critical-reasoning Bono Foundation U.K., and a former fundamentally changes how people think. operations without technology as we computer science lecturer at the Uni- can with it—just at different speeds and versity of Manchester. “Developing our formation can easily overwhelm our with different ease,” West says. abilities to think more clearly, richly, reasoning abilities.” What’s more, What’s more, while it’s tempting fully—individually and collectively— it’s ironic that ever-growing piles of to view computers, video games, and is absolutely crucial [to solving world data and information do not equate the Internet in a monolithic good or problems].” to greater knowledge and better de- bad way, the reality is that they may To be sure, history is filled with tales cision-making. -
Probabilistic Databases
Series ISSN: 2153-5418 SUCIU • OLTEANU •RÉ •KOCH M SYNTHESIS LECTURES ON DATA MANAGEMENT &C Morgan & Claypool Publishers Series Editor: M. Tamer Özsu, University of Waterloo Probabilistic Databases Probabilistic Databases Dan Suciu, University of Washington, Dan Olteanu, University of Oxford Christopher Ré,University of Wisconsin-Madison and Christoph Koch, EPFL Probabilistic databases are databases where the value of some attributes or the presence of some records are uncertain and known only with some probability. Applications in many areas such as information extraction, RFID and scientific data management, data cleaning, data integration, and financial risk DATABASES PROBABILISTIC assessment produce large volumes of uncertain data, which are best modeled and processed by a probabilistic database. This book presents the state of the art in representation formalisms and query processing techniques for probabilistic data. It starts by discussing the basic principles for representing large probabilistic databases, by decomposing them into tuple-independent tables, block-independent-disjoint tables, or U-databases. Then it discusses two classes of techniques for query evaluation on probabilistic databases. In extensional query evaluation, the entire probabilistic inference can be pushed into the database engine and, therefore, processed as effectively as the evaluation of standard SQL queries. The relational queries that can be evaluated this way are called safe queries. In intensional query evaluation, the probabilistic Dan Suciu inference is performed over a propositional formula called lineage expression: every relational query can be evaluated this way, but the data complexity dramatically depends on the query being evaluated, and Dan Olteanu can be #P-hard. The book also discusses some advanced topics in probabilistic data management such as top-kquery processing, sequential probabilistic databases, indexing and materialized views, and Monte Carlo databases. -
Understanding the Hidden Web
Introduction General Framework Different Modules Conclusion Understanding the Hidden Web Pierre Senellart Athens University of Economics & Business, 28 July 2008 Introduction General Framework Different Modules Conclusion Simple problem Contact all co-authors of Serge Abiteboul. It’s easy! You just need to: Find all co-authors. For each of them, find their current email address. Introduction General Framework Different Modules Conclusion Advanced Scholar Search Advanced Search Tips | About Google Scholar Find articles with all of the words with the exact phrase with at least one of the words without the words where my words occur Author Return articles written by e.g., "PJ Hayes" or McCarthy Publication Return articles published in e.g., J Biol Chem or Nature Date Return articles published between — e.g., 1996 Subject Areas Return articles in all subject areas. Return only articles in the following subject areas: Biology, Life Sciences, and Environmental Science Business, Administration, Finance, and Economics Chemistry and Materials Science Engineering, Computer Science, and Mathematics Medicine, Pharmacology, and Veterinary Science Physics, Astronomy, and Planetary Science Social Sciences, Arts, and Humanities ©2007 Google Introduction General Framework Different Modules Conclusion Advanced Scholar Search Advanced Search Tips | About Google Scholar Find articles with all of the words with the exact phrase with at least one of the words without the words where my words occur Author Return articles written by e.g., "PJ Hayes" or McCarthy Publication -
Autumn 2Copy2:First Draft.Qxd
news THE ROYAL SOCIETY OF EDINBURGH ISSUE 26 AUTUMN/WINTER 2009 RESOURCE THE NEWSLETTER OF SCOTLAND’ S NATIONAL ACADEMY SCOTLAND’S RESEARCH TALENT In September 2009 over 70 researchers, mostly in the early stages of their careers, were invited to attend the RSE Annual Research Awards Ceremony. The Awards were presented by RSE President, Lord Wilson of Tillyorn, KT GCMG and Professor Alan Miller, RSE Research Awards Convener. Former BP Research Fellow, Professor Miles Padgett, (pictured below right) who held his Fellowship at the University of St Andrews between 1993 and 1995, also addressed the meeting. Professor Padgett now holds a personal Chair in Physics and is head of the Optics Group at the University of Glasgow. He was elected to the Fellowship of the RSE in 2001 and served as Young People’s Convener from 2005 to 2008, remaining an active member of that programme today. Lord Wilson is pictured with Dr Sinead Rhodes from the Department of Psychology, University of St Andrews whose research proposal was granted a small project fund in the Scottish Crucible programme. Full details of all the awards can be found inside. International Links Climate Change Debate Education Programme Scotland’s Research Talent Lessells Travel Scholarships Cormack Vacation Piazzi Smyth Bequest Dr Spela Ivekovic Scholarships Research Scholarship School of Computing, University of Dundee Dominic Lawson James Henderson Swarm Intelligence and Projective Department of Physics and Astronomy, Department of Physics, Geometry for Computer Vision University of Glasgow -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
Essays Dedicated to Peter Buneman ; [Colloquim in Celebration
Val Tannen Limsoon Wong Leonid Libkin Wenfei Fan Wang-Chiew Tan Michael Fourman (Eds.) In Search of Elegance in the Theory and Practice of Computation Essays Dedicated to Peter Buneman 4^1 Springer Table of Contents Models for Data-Centric Workflows 1 Serge Abiteboul and Victor Vianu Relational Databases and Bell's Theorem 13 Samson Abramsky High-Level Rules for Integration and Analysis of Data: New Challenges 36 Bogdan Alexe, Douglas Burdick, Mauricio A. Hernandez, Georgia Koutrika, Rajasekar Krishnamurthy, Lucian Popa, Ioana R. Stanoi, and Ryan Wisnesky A New Framework for Designing Schema Mappings 56 Bogdan Alexe and Wang-Chiew Tan User Trust and Judgments in a Curated Database with Explicit Provenance 89 David W. Archer, Lois M.L. Delcambre, and David Maier An Abstract, Reusable, and Extensible Programming Language Design Architecture 112 Hassan Ait-Kaci A Discussion on Pricing Relational Data 167 Magdalena Balazinska, Bill Howe, Paraschos Koutris, Dan Suciu, and Prasang Upadhyaya Tractable Reasoning in Description Logics with Functionality Constraints 174 Andrea Call, Georg Gottlob, and Andreas Pieris Toward a Theory of Self-explaining Computation 193 James Cheney, Umut A. Acar, and Roly Perera To Show or Not to Show in Workflow Provenance 217 Susan B. Davidson, Sanjeev Khanna, and Tova Milo Provenance-Directed Chase&Backchase 227 Alin Deutsch and Richard Hull Data Quality Problems beyond Consistency and Deduplication 237 Wenfei Fan, Floris Geerts, Shuai Ma, Nan Tang, and Wenyuan Yu X Table of Contents 250 Hitting Buneman Circles Michael Paul Fourman 259 Looking at the World Thru Colored Glasses Floris Geerts, Anastasios Kementsietsidis, and Heiko Muller Static Analysis and Query Answering for Incomplete Data Trees with Constraints 273 Amelie Gheerbrant, Leonid Libkin, and Juan Reutter Using SQL for Efficient Generation and Querying of Provenance Information 291 Boris Glavic, Renee J. -
INF2032 2017-2 Tpicos Em Bancos De Dados III ”Foundations of Databases”
INF2032 2017-2 Tpicos em Bancos de Dados III "Foundations of Databases" Profs. Srgio Lifschitz and Edward Hermann Haeusler August-December 2017 1 Motivation The motivation of this course is to put together theory and practice of database and knowledge base systems. The main content includes a formal approach for the underlying existing technology from the point of view of logical expres- siveness and computational complexity. New and recent database models and systems are discussed from theory to practical issues. 2 Goal of the course The main goal is to introduce the student to the logical and computational foundations of database systems. 3 Sylabus The course in divided in two parts: Classical part: relational database model 1. Revision and formalization of relational databases. Limits of the relational data model; 2. Relational query languages; DataLog and recursion; 3. Logic: Propositional, First-Order, Higher-Order, Second-Order, Fixed points logics; 4. Computational complexity and expressiveness of database query lan- guages; New approaches for database logical models 1. XML-based models, Monadic Second-Order Logic, XPath and XQuery; 2. RDF-based knowledge bases; 3. Decidable Fragments of First-Order logic, Description Logic, OWL dialects; 4. NoSQL and NewSQL databases; 4 Evaluation The evaluation will be based on homework assignments (70 %) and a research manuscript + seminar (30%). 5 Supporting material Lecture notes, slides and articles. Referenced books are freely available online. More details at a wiki-based web page (available from August 7th on) References [ABS00] Serge Abiteboul, Peter Buneman, and Dan Suciu. Data on the Web: From Relations to Semistructured Data and XML. -
Curating the CIA World Factbook 29 the International Journal of Digital Curation Issue 3, Volume 4 | 2009
Curating the CIA World Factbook 29 The International Journal of Digital Curation Issue 3, Volume 4 | 2009 Curating the CIA World Factbook Peter Buneman, Heiko Müller, School of Informatics, University of Edinburgh Chris Rusbridge, Digital Curation Centre, University of Edinburgh Abstract The CIA World Factbook is a prime example of a curated database – a database that is constructed and maintained with a great deal of human effort in collecting, verifying, and annotating data. Preservation of old versions of the Factbook is important for verification of citations; it is also essential for anyone interested in the history of the data such as demographic change. Although the Factbook has been published, both physically and electronically, only for the past 30 years, we appear in danger of losing this history. This paper investigates the issues involved in capturing the history of an evolving database and its application to the CIA World Factbook. In particular it shows that there is substantial added value to be gained by preserving databases in such a way that questions about the change in data, (longitudinal queries) can be readily answered. Within this paper, we describe techniques for recording change in a curated database and we describe novel techniques for querying the change. Using the example of this archived curated database, we discuss the extent to which the accepted practices and terminology of archiving, curation and digital preservation apply to this important class of digital artefacts.1 1 This paper is based on the paper given by the authors at the 5th International Digital Curation Conference, December 2009; received November 2009, published December 2009. -
Author Template for Journal Articles
Mining citation information from CiteSeer data Dalibor Fiala University of West Bohemia, Univerzitní 8, 30614 Plzeň, Czech Republic Phone: 00420 377 63 24 29, fax: 00420 377 63 24 01, email: [email protected] Abstract: The CiteSeer digital library is a useful source of bibliographic information. It allows for retrieving citations, co-authorships, addresses, and affiliations of authors and publications. In spite of this, it has been relatively rarely used for automated citation analyses. This article describes our findings after extensively mining from the CiteSeer data. We explored citations between authors and determined rankings of influential scientists using various evaluation methods including cita- tion and in-degree counts, HITS, PageRank, and its variations based on both the citation and colla- boration graphs. We compare the resulting rankings with lists of computer science award winners and find out that award recipients are almost always ranked high. We conclude that CiteSeer is a valuable, yet not fully appreciated, repository of citation data and is appropriate for testing novel bibliometric methods. Keywords: CiteSeer, citation analysis, rankings, evaluation. Introduction Data from CiteSeer have been surprisingly little explored in the scientometric lite- rature. One of the reasons for this may have been fears that the data gathered in an automated way from the Web are inaccurate – incomplete, erroneous, ambiguous, redundant, or simply wrong. Also, the uncontrolled and decentralized nature of the Web is said to simplify manipulating and biasing Web-based publication and citation metrics. However, there have been a few attempts at processing the Cite- Seer data which we will briefly mention. Zhou et al. -
Fast Data Analytics by Learning
Fast Data Analytics by Learning by Yongjoo Park A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2017 Doctoral Committee: Associate Professor Michael Cafarella, Co-Chair Assistant Professor Barzan Mozafari, Co-Chair Associate Professor Eytan Adar Professor H. V. Jagadish Associate Professor Carl Lagoze Yongjoo Park [email protected] ORCID iD: 0000-0003-3786-6214 c Yongjoo Park 2017 To my mother ii Acknowledgements I thank my co-advisors, Michael Cafarella and Barzan Mozafari. I am deeply grateful that I could start my graduate studies with Mike Cafarella. Mike was the one of the nicest persons I have met (I can say, throughout my life). He also deeply cared of my personal life as well as my graduate study. He always encouraged and supported me so I could make the best decisions for my life. He was certainly more than a simple academic advisor. Mike also has excellent talents in presentation. His descriptions (on any subject) are well-organized and straightforward. Sometimes, I find myself trying to imitate his presentation styles when I give my own presentations, which, I believe, is natural since I have been advised by him for more than five years now. I started work with Barzan after a few years since I came to Michigan. He is excellent in writing research papers and presenting work in the most interesting, formal, and concise way. He devoted tremendous amount of time for me. I learned great amount of writing and presentation skills from him. -
Transforming Databases with Recursive Data Structures
TRANSFORMING DATABASES WITH RECURSIVE DATA STRUCTURES Anthony Kosky A DISSERTATION in COMPUTER AND INFORMATION SCIENCE Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. 1996 Susan Davidson— Supervisor of Dissertation Peter Buneman— Supervisor of Dissertation Peter Buneman— Graduate Group Chairperson c Copyright 2003 by Anthony Kosky iii To my parents. iv v WARRANTY Congratulations on your acquisition of this dissertation. In acquiring it you have shown yourself to be a computer scientist of exceptionally good taste with a true appreciation for quality. Each proof, algorithm or definition in this dissertation has been carefully checked by hand to ensure correctness and reliability. Each word and formula has been meticulously crafted using only the highest quality symbols and characters. The colours of inks and paper have been carefully chosen and matched to maximize contrast and readability. The author is confident that this dissertation will provide years of reliable and trouble free ser- vice, and offers the following warranty for the lifetime of the original owner: If at any time a proof or algorithm should be found to be defective or contain bugs, simply return your disser- tation to the author and it will be repaired or replaced (at the author’s choice) free of charge. Please note that this warranty does not cover damage done to the dissertation through normal wear-and-tear, natural disasters or being chewed by family pets. This warranty is void if the dissertation is altered or annotated in any way. Concepts described in this dissertation may be new and complicated. -
A Survey on Scientific Data Management
A Survey on Scientific Data Management References [1] Ilkay Altintas, Chad Berkley, Efrat Jaeger, Matthew Jones, Bertram Ludascher,¨ and Steve Mock. Kepler: An extensible system for design and execution of scientific workflows. In Proceedings of the 16th International Conference on Scientific and Statistical Database Management (SSDBM’04), pages 21– 23, 2004. [2] Alexander Ames, Nikhil Bobb, Scott A. Brandt, Adam Hiatt, Carlos Maltzahn, Ethan L. Miller, Alisa Neeman, and Deepa Tuteja. Richer file system metadata using links and attributes. In Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies (MSST’05), pages 49–60, 2005. [3] Bill Anderson. Mass storage system performance prediction using a trace-driven simulator. In MSST ’05: Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Technologies, pages 297–306, Washington, DC, USA, 2005. IEEE Computer Society. [4] Phil Andrews, Bryan Banister, Patricia Kovatch, Chris Jordan, and Roger Haskin. Scaling a global file system to the greatest possible extent, performance, capacity, and number of users. In MSST ’05: Proceedings of the 22nd IEEE / 13th NASA Goddard Conference on Mass Storage Systems and Tech- nologies, pages 109–117, Washington, DC, USA, 2005. IEEE Computer Society. [5] Grigoris Antoniou and Frank van Harmelen. A Semantic Web Primer. The MIT Press, 2004. [6] Scott Brandt, Carlos Maltzahn, Neoklis Polyzotis, and Wang-Chiew Tan. Fusing data management services with file systems. In PDSW ’09: Proceedings of the 4th Annual Workshop on Petascale Data Storage, pages 42–46, New York, NY, USA, 2009. ACM. [7] Peter Buneman, Adriane Chapman, and James Cheney.