Foundations of Databases
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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. -
GRAPH DATABASE THEORY Comparing Graph and Relational Data Models
GRAPH DATABASE THEORY Comparing Graph and Relational Data Models Sridhar Ramachandran LambdaZen © 2015 Contents Introduction .................................................................................................................................................. 3 Relational Data Model .............................................................................................................................. 3 Graph databases ....................................................................................................................................... 3 Graph Schemas ............................................................................................................................................. 4 Selecting vertex labels .............................................................................................................................. 4 Examples of label selection ....................................................................................................................... 4 Drawing a graph schema ........................................................................................................................... 6 Summary ................................................................................................................................................... 7 Converting ER models to graph schemas...................................................................................................... 9 ER models and diagrams .......................................................................................................................... -
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. -
Bibliography
Bibliography [57391] ISO/IEC JTC1/SC21 N 5739. Database language SQL, April 1991. [69392] ISO/IEC JTC1/SC21 N 6931. Database language SQL (SQL3), June 1992. [A+76] M. M. Astrahan et al. System R: a relational approach to data management. ACM Trans. on Database Systems, 1(2):97–137, 1976. [AA93] P. Atzeni and V. De Antonellis. Relational Database Theory. Benjamin/Cummings Publishing Co., Menlo Park, CA, 1993. [AABM82] P. Atzeni, G. Ausiello, C. Batini, and M. Moscarini. Inclusion and equivalence between relational database schemata. Theoretical Computer Science, 19:267–285, 1982. [AB86] S. Abiteboul and N. Bidoit. Non first normal form relations: An algebra allowing restructuring. Journal of Computer and System Sciences, 33(3):361–390, 1986. [AB87a] M. Atkinson and P. Buneman. Types and persistence in database programming languages. ACM Computing Surveys, 19(2):105–190, June 1987. [AB87b] P. Atzeni and M. C. De Bernardis. A new basis for the weak instance model. In Proc. ACM Symp. on Principles of Database Systems, pages 79–86, 1987. [AB88] S. Abiteboul and C. Beeri. On the manipulation of complex objects. Technical Report, INRIA and Hebrew University, 1988. (To appear, VLDB Journal.) [AB91] S. Abiteboul and A. Bonner. Objects and views. In Proc. ACM SIGMOD Symp. on the Management of Data, 1991. [ABD+89] M. Atkinson, F. Bancilhon, D. DeWitt, K. Dittrich, D. Maier, and S. Zdonik. The object- oriented database system manifesto. In Proc. of Intl. Conf. on Deductive and Object-Oriented Databases (DOOD), pages 40–57, 1989. [ABGO93] A. Albano, R. Bergamini, G. Ghelli, and R. Orsini. -
Formal Languages and Logics Lecture Notes
Formal Languages and Logics Jacobs University Bremen Herbert Jaeger Course Number 320211 Lecture Notes Version from Oct 18, 2017 1 1. Introduction 1.1 What these lecture notes are and aren't This lecture covers material that is relatively simple, extremely useful, superbly described in a number of textbooks, and taught in almost precisely the same way in hundreds of university courses. It would not make much sense to re-invent the wheel and re-design this lecture to make it different from the standards. Thus I will follow closely the classical textbook of Hopcroft, Motwani, and Ullman for the first part (automata and formal languages) and, somewhat more freely, the book of Schoening in the second half (logics). Both books are available at the IRC. These books are not required reading; the lecture notes that I prepare are a fully self-sustained reference for the course. However, I would recommend to purchase a personal copy of these books all the same for additional and backup reading because these are highly standard reference books that a computer scientist is likely to need again later in her/his professional life, and they are more detailed than this lecture or these lecture notes. There are several sets of course slides and lecture notes available on the Web which are derived from the Hopcroft/Motwani/Ullman book. At http://www- db.stanford.edu/~ullman/ialc/win00/win00.html, you will find lecture notes prepared by Jeffrey Ullman himself. Given the highly standardized nature of the material and the availablity of so many good textbooks and online lecture notes, these lecture notes of mine do not claim originality. -
Data Definition Language (Ddl)
DATA DEFINITION LANGUAGE (DDL) CREATE CREATE SCHEMA AUTHORISATION Authentication: process the DBMS uses to verify that only registered users access the database - If using an enterprise RDBMS, you must be authenticated by the RDBMS - To be authenticated, you must log on to the RDBMS using an ID and password created by the database administrator - Every user ID is associated with a database schema Schema: a logical group of database objects that are related to each other - A schema belongs to a single user or application - A single database can hold multiple schemas that belong to different users or applications - Enforce a level of security by allowing each user to only see the tables that belong to them Syntax: CREATE SCHEMA AUTHORIZATION {creator}; - Command must be issued by the user who owns the schema o Eg. If you log on as JONES, you can only use CREATE SCHEMA AUTHORIZATION JONES; CREATE TABLE Syntax: CREATE TABLE table_name ( column1 data type [constraint], column2 data type [constraint], PRIMARY KEY(column1, column2), FOREIGN KEY(column2) REFERENCES table_name2; ); CREATE TABLE AS You can create a new table based on selected columns and rows of an existing table. The new table will copy the attribute names, data characteristics and rows of the original table. Example of creating a new table from components of another table: CREATE TABLE project AS SELECT emp_proj_code AS proj_code emp_proj_name AS proj_name emp_proj_desc AS proj_description emp_proj_date AS proj_start_date emp_proj_man AS proj_manager FROM employee; 3 CONSTRAINTS There are 2 types of constraints: - Column constraint – created with the column definition o Applies to a single column o Syntactically clearer and more meaningful o Can be expressed as a table constraint - Table constraint – created when you use the CONTRAINT keyword o Can apply to multiple columns in a table o Can be given a meaningful name and therefore modified by referencing its name o Cannot be expressed as a column constraint NOT NULL This constraint can only be a column constraint and cannot be named. -
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. -
A Dictionary of DBMS Terms
A Dictionary of DBMS Terms Access Plan Access plans are generated by the optimization component to implement queries submitted by users. ACID Properties ACID properties are transaction properties supported by DBMSs. ACID is an acronym for atomic, consistent, isolated, and durable. Address A location in memory where data are stored and can be retrieved. Aggregation Aggregation is the process of compiling information on an object, thereby abstracting a higher-level object. Aggregate Function A function that produces a single result based on the contents of an entire set of table rows. Alias Alias refers to the process of renaming a record. It is alternative name used for an attribute. 700 A Dictionary of DBMS Terms Anomaly The inconsistency that may result when a user attempts to update a table that contains redundant data. ANSI American National Standards Institute, one of the groups responsible for SQL standards. Application Program Interface (API) A set of functions in a particular programming language is used by a client that interfaces to a software system. ARIES ARIES is a recovery algorithm used by the recovery manager which is invoked after a crash. Armstrong’s Axioms Set of inference rules based on set of axioms that permit the algebraic mani- pulation of dependencies. Armstrong’s axioms enable the discovery of minimal cover of a set of functional dependencies. Associative Entity Type A weak entity type that depends on two or more entity types for its primary key. Attribute The differing data items within a relation. An attribute is a named column of a relation. Authorization The operation that verifies the permissions and access rights granted to a user.