Example-Guided Synthesis of Relational Queries
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Pharmakologie Und Medizinische Chemie Uracil- Und Uracilnucleotid-Bindender Membranproteine
Pharmakologie und Medizinische Chemie Uracil- und Uracilnucleotid-bindender Membranproteine Dissertation zur Erlangung des Doktorgrades (Dr. rer. nat.) der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn vorgelegt von Anja B. Scheiff aus Aachen Bonn 2010 Angefertigt mit Genehmigung der Mathematisch-Naturwissenschaftlichen Fakultät der Rheinischen Friedrich-Wilhelms-Universität Bonn 1. Gutachter: Prof. Dr. Christa E. Müller 2. Gutachter: Prof. Dr. Gerd Bendas Tag der Promotion: 18.08.2010 Erscheinungsjahr: 2010 Diese Dissertation ist auf dem Hochschulserver der ULB Bonn http://hss.ulb.uni- bonn.de/diss_online elektronisch publiziert. Die vorliegende Arbeit wurde in der Zeit von August 2005 bis Juni 2010 am Pharmazeutischen Institut der Rheinischen Friedrich-Wilhelms-Universität Bonn unter der Leitung von Frau Prof. Dr. Christa E. Müller durchgeführt. Mein besonderer Dank gilt Frau Professor Dr. Christa E. Müller für die Überlassung des vielseitigen und sehr interessanten Promotionsthemas. Ich bedanke mich für die freundliche Betreuung, die stete Dikussionsbereitschaft und die zahlreichen Anregungen und Hilfe- stellungen, die wesentlich zum Gelingen dieser Arbeit beigetragen haben. Herrn Professor Dr. Gerd Bendas danke ich für die freundliche Übernahme des Koreferates. Für die Mitwirkung in meiner Prüfungskommission bedanke ich mich bei Herrn PD Dr. Hubert Rein und Frau Professor Dr. Gabriele Bierbaum. Meiner Familie „Jeder junge Wissenschaftler sollte stets die Möglichkeit im Auge behalten, -
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 -
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. -
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. -
Council Amends, Then Oks Decree to Build Deck, Decks H> PAH.J.PEYTON Mcdermott Broke a 4-4 Deadlock
ISPS MMM20 Ol K 11 Ith YEAR - ISSUE NO. 37-111 Thursday, May 24. IIHli Published tsrrs Ihursdas Periodical - Postage Paid al « cMfU ld. NJ. Sinn' IHKI (908) 232-4407 FIFTY ( ENTS Council Amends, Then OKs Decree to Build Deck, Decks H> PAH.J.PEYTON McDermott broke a 4-4 deadlock. more spaces. Specially tonne* foe The W rtifaU l code • The ordinance on Tuesday was Councilman Sullivan said four of After hearing arguments over the passed following an amendment by the 11 so-called "parking principals." Course of four and a half hours, both Third Ward Councilman Neil F. approved last summer by the coun pro and con. ihe Town Council unani Sullivan, who chairs the Transporta cil, have been implemented to date. mously passed an ordinance Tues tion. Parking and Traffic Commit He said the town anticipates that the day night setting up a funding source tee, which changed the ordinance to valet parking and jitney service will of $700,000 for the design, construc reflect the possibility of building not be up and running by the fall. tion management and related profes- just one deck, as included in the Town Administrator Thomas B. iional serv ices for the building of a original ordinance, but two decks. Shannon announced that interviews parking deck or decks in the down- In addition, the document reflects among the remaining eight candi tow n that funds can be spent on other dates for (he newly created position " The vote occurred at I a.m. means of improving the parking situ of parking manager will he conducted Wednesday following comments ation within the town. -
Office of the Chief Commissioner of CGST& Central Excise (Chandigarh Zone), Central Revenue Building, Sector 17-C Chandigarh
/ Office of the Chief Commissioner of Department of Excise and Taxation CGST& Central Excise Additional Town hall Building (Chandigarh Zone), Sector-17-C, UT Chandigarh Central Revenue Building, Sector 17-C Chandigarh-160017 Order 03/2017 Dated 20.12.2017 Subject: Division of Taxpayers base between the Central Government and Union Territory of Chandigarh In accordance with the guidelines issued by the GST Council Secretariat vide Circular No. 01/2017, issued vide F. No. 166/Cross Empowerment/GSTC/2017 dated 20.09.2017, with respect to the division of taxpayer base between the Central Government and Union Territory of Chandigarh to ensure single interface under GST, the State Level Committee comprising Ms. Manoranjan Kaur Virk, Chief Commissioner, Central Tax and Central Excise, Chandigarh Zone and Shri Ajit Balaji Joshi, Commissioner, Excise and Taxation Department, UT Chandigarh has hereby decided to assign the taxpayers registered in Union Territory of Chandigarh in the following manner: 1. Taxpayers with turnover above Rs l.S Crores. a) Taxpayers falling under the jurisdiction of the Centre (List of 2166 Taxpayers enclosed as Annexure- 'lA') SI. NO. Trade Name GSTIN 1 BANK OF BARODA 04AAACB1534F1ZE 2 INDIAN OVERSEAS BANK 04AAACI1223J2Z3 ---------- 2166 DASHMESH TRADING COMPANY 04AAAFD7732Q1Z7 b) Taxpayers falling under the jurisdiction of Union Territory of Chandigarh (List of 2162 Taxpayers enclosed as Annexure- 'lB') SI. NO. Trade Name GSTIN 1 IBM INDIA PRIVATE LIMITED 04AAACI4403L1ZW 2 INTERGLOBE AVIATION LIMITED 04AABCI2726B1ZA ---------- 2162 HARJINDER SINGH 04ABXPS8524P1ZK Taxpayers with Turnover less than Rs. 1.5 Crores a) Taxpayers falling under the jurisdiction of the Centre (List of 1629 Taxpayers enclosed as Annexure- '2A') 51. -
Transcription and Analysis of Ravi Shankar's Morning Love For
Louisiana State University LSU Digital Commons LSU Doctoral Dissertations Graduate School 2013 Transcription and analysis of Ravi Shankar's Morning Love for Western flute, sitar, tabla and tanpura Bethany Padgett Louisiana State University and Agricultural and Mechanical College, [email protected] Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_dissertations Part of the Music Commons Recommended Citation Padgett, Bethany, "Transcription and analysis of Ravi Shankar's Morning Love for Western flute, sitar, tabla and tanpura" (2013). LSU Doctoral Dissertations. 511. https://digitalcommons.lsu.edu/gradschool_dissertations/511 This Dissertation is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Doctoral Dissertations by an authorized graduate school editor of LSU Digital Commons. For more information, please [email protected]. TRANSCRIPTION AND ANALYSIS OF RAVI SHANKAR’S MORNING LOVE FOR WESTERN FLUTE, SITAR, TABLA AND TANPURA A Written Document Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Doctor of Musical Arts in The School of Music by Bethany Padgett B.M., Western Michigan University, 2007 M.M., Illinois State University, 2010 August 2013 ACKNOWLEDGEMENTS I am entirely indebted to many individuals who have encouraged my musical endeavors and research and made this project and my degree possible. I would first and foremost like to thank Dr. Katherine Kemler, professor of flute at Louisiana State University. She has been more than I could have ever hoped for in an advisor and mentor for the past three years. -
Data on the Web: from Relations to Semistructured Data and XML
Data on the Web: From Relations to Semistructured Data and XML Serge Abiteboul Peter Buneman Dan Suciu February 19, 2014 1 Data on the Web: from Relations to Semistructured Data and XML Serge Abiteboul Peter Buneman Dan Suciu An initial draft of part of the book Not for distribution 2 Contents 1 Introduction 7 1.1 Audience . 8 1.2 Web Data and the Two Cultures . 9 1.3 Organization . 15 I Data Model 17 2 A Syntax for Data 19 2.1 Base types . 21 2.2 Representing Relational Databases . 21 2.3 Representing Object Databases . 22 2.4 Specification of syntax . 26 2.5 The Object Exchange Model, OEM . 26 2.6 Object databases . 27 2.7 Other representations . 30 2.7.1 ACeDB . 30 2.8 Terminology . 32 2.9 Bibliographic Remarks . 34 3 XML 37 3.1 Basic syntax . 39 3.1.1 XML Elements . 39 3.1.2 XML Attributes . 41 3.1.3 Well-Formed XML Documents . 42 3.2 XML and Semistructured Data . 42 3.2.1 XML Graph Model . 43 3.2.2 XML References . 44 3 4 CONTENTS 3.2.3 Order . 46 3.2.4 Mixing elements and text . 47 3.2.5 Other XML Constructs . 47 3.3 Document Type Declarations . 48 3.3.1 A Simple DTD . 49 3.3.2 DTD's as Grammars . 49 3.3.3 DTD's as Schemas . 50 3.3.4 Declaring Attributes in DTDs . 52 3.3.5 Valid XML Documents . 54 3.3.6 Limitations of DTD's as schemas . -
O. Peter Buneman Curriculum Vitæ – Jan 2008
O. Peter Buneman Curriculum Vitæ { Jan 2008 Work Address: LFCS, School of Informatics University of Edinburgh Crichton Street Edinburgh EH8 9LE Scotland Tel: +44 131 650 5133 Fax: +44 667 7209 Email: [email protected] or [email protected] Home Address: 14 Gayfield Square Edinburgh EH1 3NX Tel: 0131 557 0965 Academic record 2004-present Research Director, Digital Curation Centre 2002-present Adjunct Professor of Computer Science, Department of Computer and Information Science, University of Pennsylvania. 2002-present Professor of Database Systems, Laboratory for the Foundations of Computer Science, School of Informatics, University of Edinburgh 1990-2001 Professor of Computer Science, Department of Computer and Information Science, University of Pennsylvania. 1981-1989 Associate Professor of Computer Science, Department of Computer and Information Science, University of Pennsylvania. 1981-1987 Graduate Chairman, Department of Computer and Information Science, University of Pennsyl- vania 1975-1981 Assistant Professor of Computer Science at the Moore School and Assistant Professor of Decision Sciences at the Wharton School, University of Pennsylvania. 1969-1974 Research Associate and subsequently Lecturer in the School of Artificial Intelligence, Edinburgh University. Education 1970 PhD in Mathematics, University of Warwick (Supervisor E.C. Zeeman) 1966 MA in Mathematics, Cambridge. 1963-1966 Major Scholar in Mathematics at Gonville and Caius College, Cambridge. Awards, Visting positions Distinguished Visitor, University of Auckland, 2006 Trustee, VLDB Endowment, 2004 Fellow of the Royal Society of Edinburgh, 2004 Royal Society Wolfson Merit Award, 2002 ACM Fellow, 1999. 1 Visiting Resarcher, INRIA, Jan 1999 Visiting Research Fellowship sponsored by the Japan Society for the Promotion of Science, Research Institute for the Mathematical Sciences, Kyoto University, Spring 1997. -
SIGMOD Record’S Series of Interviews with Distinguished Members of the Database Community
Serge Abiteboul Speaks Out on Building a Research Group in Europe, How He Got Involved in a Startup, Why Systems Papers Shouldn’t Have to Include Measurements, the Value of Object Databases, and More by Marianne Winslett http://www-rocq.inria.fr/~abitebou/ Welcome to this installment of ACM SIGMOD Record’s series of interviews with distinguished members of the database community. I’m Marianne Winslett, and today we are at the SIGMOD 2006 conference in Chicago, Illinois. I have here with me Serge Abiteboul, who is a senior researcher at INRIA and the manager of the Gemo Database Group. Serge’s research interests are in databases, web data, and database theory. He received the SIGMOD Innovation Award in 1998, and he is a cofounder of Xyleme, a company that provides XML-based content management. His PhD is from the University of Southern California. So, Serge, welcome! Serge, you have built what may be the most successful database group in Europe. How did you do it? And are the challenges for building a group different in Europe than they would be in the US? Many people deserve credit for the group’s success. It is a continuation of a research group named Verso that was sponsored by Francois Bancilhon and Michel Scholl; so when I arrived, the group was already existing. My contribution was to bring in some new fresh very good scientists --- Luc Segoufin, Ioana Manolescu, Sophie Cluet. More recently, we moved to a new research unit of INRIA. The idea was to get closer to the university, so now we are at the University of Orsay; we merged with the knowledge representation group that was managed by Christina Rousset.