Ioana Manolescu

Active XML: peer-to-peer data and web services integration

Publisher Summary The content of an Active eXtensible Markup Language (AXML) document is dynamic, because it is possible to specify when a service call should be activated (for example, when needed, every hour, etc.), and for how long its result should be considered valid. Thus, this simple mechanism allows capturing and combining different styles of data integration, such as warehousing and mediation. To fully take advantage of the use of services, AXML also allows calling continuous services (that provide streams of answers) and services supporting intentional data (AXML document including service calls) as parameters and/or result. The latter feature leads to powerful, recursive integration schemes. The AXML framework is centered on AXML documents , which are XML documents that may contain calls to Web services. When calls included in an AXML document are fired, the latter is enriched by the corresponding results.

XML processing in DHT networks

We study the scalable management of XML data in P2P networks based on distributed hash tables (DHTs). We identify performance limitations in this context, and propose an array of techniques to lift them. First, we adapt the DHT platforms index store and communication primitives to the needs of massive data processing. Second, we introduce a distributed hierarchical index and associated efficient algorithms to speed up query processing. Third, we present an innovative, XML-specific flavor of Bloom filters, to reduce data transfers entailed by query processing. Our approach is fully implemented in the KadoP system, used in a real-life software manufacturing application. Our experiments demonstrate the benefits of the proposed techniques.

Managing distributed workspaces with active XML

Publisher Summary This chapter discusses the distributed workspaces with active XML. The tremendous evolution of the Web has brought the need for platforms allowing to easily deploy distributed data management applications. The current trend goes towards the de-centralization of such platforms, and in particular to peer-to-peer architectures. The active XML system provides a peer-to-peer data integration platform, based on Web standards such as XML, and Web services. The system is centered on Active XML system (AXML) documents: XML documents where parts of the content are explicit XML data, whereas other parts are dynamically generated by calls to Web services on the same or on other peers. By including web service calls, AXML documents already have an inherent form of distributed computation.

Chapter 13: liquid queries and liquid results in search computing

Liquid queries are a flexible tool for information seeking, based on the progressive exploration of the search space; they produce “fluid” results which dynamically adapt to the shape of the query, as a liquid adapts to its container. The liquid query paradigm relies on the SeCo service mart and multi-domain query execution concepts: an expert user selects a priori the service marts relevant to the information seeking task at hand and the connections necessary to join them, and publishes such a definition in the SeCo back-end. The Liquid Query client-side interface consumes the application definition created by the expert and dynamically builds a query interface for the end-user. Such interface allows one to supply keywords to query the pre-configured service marts and offers controls for exploring the combinations computed by the SeCo execution engine. The interaction commands are based on a tabular representation of results and comprise: reordering, clustering, addition or deletion of attributes, addition of extra service marts to the query for specific items in the result set or for the entire result set, request of more results from all services or from selected ones, expansion of details on selected items, and more. The Liquid Query is equipped with multiple data visualization options suited to render multi-domain results and can be instrumented with indicators showing the quality of the result set.

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

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Distributed Computing with MapReduce and Pig

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Web Data Management: Distributed Access Structures

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