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Author

Serge Abiteboul

Bio: Serge Abiteboul is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topic(s): XML & Query language. The author has an hindex of 73, co-authored 278 publication(s) receiving 24576 citation(s). Previous affiliations of Serge Abiteboul include University of Southern California & PSL Research University.
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Book
02 Dec 1994
TL;DR: This book discusses Languages, Computability, and Complexity, and the Relational Model, which aims to clarify the role of Semantic Data Models in the development of Query Language Design.
Abstract: A. ANTECHAMBER. Database Systems. The Main Principles. Functionalities. Complexity and Diversity. Past and Future. Ties with This Book. Bibliographic Notes. Theoretical Background. Some Basics. Languages, Computability, and Complexity. Basics from Logic. The Relational Model. The Structure of the Relational Model. Named versus Unnamed Perspectives. Notation. Bibliographic Notes. B. BASICS: RELATIONAL QUERY LANGUAGES. Conjunctive Queries. Getting Started. Logic-Based Perspectives. Query Composition and Views. Algebraic Perspectives. Adding Union. Bibliographic Notes. Exercises. Adding Negation: Algebra and Calculus. The Relational Algebras. Nonrecursive Datalog with Negation. The Relational Calculus. Syntactic Restrictions for Domain Independence. Aggregate Functions. Digression: Finite Representations of Infinite Databases. Bibliographic Notes. Exercises. Static Analysis and Optimization. Issues in Practical Query Optimization. Global Optimization. Static Analysis of the Relational Calculus. Computers with Acyclic Joins. Bibliographic Notes. Exercises. Notes on Practical Languages. SQL: The Structured Query Language. Query-by-Example and Microsoft Access. Confronting the Real World. Bibliographic Notes. Exercises. C. CONSTRAINTS. Functional and Join Dependency. Motivation. Functional and Key Dependencies. join and Multivalued Dependencies. The Chase. Bibliographic Notes. Exercises. Inclusion Dependency. Inclusion Dependency in Isolation. Finite versus Infinite Implication. Nonaxiomatizability of fd's + ind's. Restricted Kinds of Inclusion Dependency. Bibliographic Notes. Exercises. A Larger Perspective. A Unifying Framework. The Chase revisited. Axiomatization. An Algebraic Perspective. Bibliographic Notes. Exercises. Design and Dependencies. Semantic Data Models. Normal Forms. Universal Relation Assumption. Bibliographic Notes. Exercises. D. DATALOG AND RECURSION. Datalog. Syntax of Datalog. Model-Theoretic Semantics. Fixpoint Semantics. Proof-Theoretic Approach. Static Program Analysis. Bibliographic Notes. Exercises. Evaluation of Datalog. Seminaive Evaluation. Top-Down Techniques. Magic. Two Improvements. Bibliographic Notes. Exercises. Recursion and Negation. Algebra + While. Calculus + Fixpoint. Datalog with Negation. Equivalence. Recursion in Practical Language. Bibliographic Notes. Exercises. Negation in Datalog. The Basic Problem. Stratified Semantics. Well-Founded Semantics. Expressive Power. Negation as Failure of Brief. Bibliographic Notes. Exercises. E. EXPRESSIVENESS AND COMPLEXITY. Sizing up Languages. Queries. Complexity of Queries. Languages and Complexity. Bibliographic Notes. Exercises. First Order, Fixpoint and While. Complexity of First-Order Queries. Expressiveness of First-Order Queries. Fixpoint and While Queries. The Impact of Order. Bibliographic Notes. Exercises. Highly Expressive Languages. While(N)-while with Arithmetic. While(new)-while with New Values. While(uty)-An Untyped Extension of while. Bibliographic Notes. Exercises. F. FINALE. Incomplete Information. Warm-Up. Weak Representation Systems. Conditional Tables. The Complexity of Nulls. Other Approaches. Bibliographic Notes. Exercises. Complex Values. Complex Value Databases. The Algebra. The Caculas. Examples. Equivalence Theorems. Fixpoint and Deduction. Expressive Power and Complexity. A Practicle Query Language for Complex Values. Bibliographic Notes. Exercises. Object Databases. Informal Presentation. Formal Definition of an OODB Model. Languages for OODB Queries. Languages for Methods. Further Issues for OODB's. Bibliographic Notes. Exercises. Dynamic Aspects. Updated Languages. Transactional Schemas. Updating Views and Deductive Databases. Active Databases. Temporal Databases and Constraints. Bibliographic Notes. Exercises. Bibliography. Symbol Index. Index. 0201537710T04062001

4,231 citations


Journal ArticleDOI
Serge Abiteboul1, Dallan Quass1, Jason G. McHugh1, Jennifer Widom1  +1 moreInstitutions (1)
TL;DR: The main novelties of the Lorel language are the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user.
Abstract: language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular: some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a user-friendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of the ODMG data model and the Lorel language as an extension of OQL. The main novelties of the Lorel language are: (i) the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and (ii) powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user. Lorel also includes a declarative update language. Lorel is implemented as the query language of the Lore prototype database management system at Stanford. Information about Lore can be found at http://www-db.stanford.edu/lore. In addition to presenting the Lorel language in full, this paper briefly describes the Lore system and query processor. We also briefly discuss a second implementation of Lorel on top of a conventional object-oriented database management system, the O2 system.

1,249 citations


Book
Serge Abiteboul1, Peter Buneman2, Dan Suciu3Institutions (3)
21 Oct 1999
TL;DR: A Syntax for Data: Typing semistructured data and the Lore system and database products supporting XML are explained.
Abstract: 1 Introduction 2 A Syntax for Data 3 XML 4 Query Languages 5 Query Languages for XML 6 Interpretation and advanced features 7 Typing semistructured data 8 Query Processing 9 The Lore system 10 Strudel 11 Database products supporting XML

1,190 citations


Book ChapterDOI
Serge Abiteboul1Institutions (1)
08 Jan 1997
TL;DR: The main purpose of the paper is to isolate the essential aspects of semistructured data, and survey some proposals of models and query languages for semi-structured data.
Abstract: The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in the systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specic interfaces, or data exchange formats. Some of this data is raw data, e.g., images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases. As will seen later when the notion of semi-structured data is more precisely de ned, the need for semi-structured data arises naturally in the context of data integration, even when the data sources are themselves well-structured. Although data integration is an old topic, the need to integrate a wider variety of data- formats (e.g., SGML or ASN.1 data) and data found on the Web has brought the topic of semi-structured data to the forefront of research. The main purpose of the paper is to isolate the essential aspects of semi- structured data. We also survey some proposals of models and query languages for semi-structured data. In particular, we consider recent works at Stanford U. and U. Penn on semi-structured data. In both cases, the motivation is found in the integration of heterogeneous data.

867 citations


Journal ArticleDOI
Jason G. McHugh1, Serge Abiteboul1, Roy Goldman1, Dallas Quass1  +1 moreInstitutions (1)
01 Sep 1997
TL;DR: This paper provides an overview of these aspects of the Lore system, as well as other novel features such as dynamic structural summaries and seamless access to data from external sources.
Abstract: Lore (for Lightweight Object Repository) is a DBMS designed specifically for managing semistructured information. Implementing Lore has required rethinking all aspects of a DBMS, including storage management, indexing, query processing and optimization, and user interfaces. This paper provides an overview of these aspects of the Lore system, as well as other novel features such as dynamic structural summaries and seamless access to data from external sources.

689 citations


Cited by
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Book
Jiawei Han1, Micheline Kamber2, Jian Pei2Institutions (2)
08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

23,590 citations


Journal ArticleDOI
Sergey Brin1, Lawrence Page1Institutions (1)
01 Apr 1998
TL;DR: This paper provides an in-depth description of Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext and looks at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

14,045 citations


Journal Article
Abstract: In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/. To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want.

13,327 citations


01 Jan 2002

8,563 citations



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Performance
Metrics

Author's H-index: 73

No. of papers from the Author in previous years
YearPapers
20202
20192
20185
20179
20163
20153