Serge Abiteboul

Deciding properties of transactional schemas

When the admissible operations on a database are specified using transactional schemas, certain properties of such schemas are desirable. Unfort,unat.ely, most of these ,properties are undecidable. Such properties are systemat.ically examined in this paper. The results concern restrictions on transact,ions and on properties, which imply deridability. Some of the results are surprising. and provide new insight into the nature of relational transactions.

Web Data Management: Ontologies, RDF, and OWL

INTRODUCTION The vision of the Semantic Web is that of a world-wide distributed architecture where data and services easily interoperate. This vision is not yet a reality in the Web of today, in which given a particular need, it is difficult to find a resource that is appropriate to it. Also, given a relevant resource, it is not easy to understand what it provides and how to use it. To solve such limitations, facilitate interoperability, and thereby enable the Semantic Web vision, the key idea is to also publish semantics descriptions of Web resources. These descriptions rely on semantic annotations , typically on logical assertions that relate resources to some terms in predefined ontologies . This is the topic of the chapter. An ontology is a formal description providing human users a shared understanding of a given domain. The ontologies we consider here can also be interpreted and processed by machines thanks to a logical semantics that enables reasoning. Ontologies provide the basis for sharing knowledge, and, as such, they are very useful for a number of reasons: Organizing data. It is very easy to get lost in large collections of documents. An ontology is a natural means of “organizing” (structuring) it and thereby facilitates browsing through it to find interesting information. It provides an organization that is flexible, and that naturally structures the information in multidimensional ways. For instance, an ontology may allow browsing through the courses offered by a university by topic or department, by quarter or time, by level, and so forth.

Putting into Practice: Large-Scale Data Management with Hadoop

The chapter proposes an introduction to HADOOP and suggests some exercises to initiate a practical experience of the system. The following assumes that you dispose of a Unixlike system (Mac OS X works just fine; Windows requires Cygwin). HADOOP can run in a pseudo-distributed mode which does not require a cluster infrastructure for testing the software, and the main part of our instructions considers this mode. Switching to a real cluster requires some additional configurations that are introduced at the end of the chapter. Since HADOOP is a relatively young system that steadily evolves, looking at the on-line, up-to-date documentation is of course recommended if you are to use it on a real basis. We illustrate HADOOP, MAPREDUCE and PIG manipulations on the DBLP data set, which can be retrieved from the following URL:

Web Data Management: An Introduction to Distributed Systems

For personal use only, not for distribution.

Putting into Practice: Wrappers and Data Extraction with XSLT

For personal use only, not for distribution.

Distributed Computing with MapReduce and Pig

For personal use only, not for distribution.

Web Data Management: Distributed Access Structures

For personal use only, not for distribution.