A Programming Language for Web Service Development

There is now widespread acceptance of Web services and service-oriented architectures. But despite the agreement on key Web services standards there remain many challenges. Programming environments based on WSDL support go some way to facilitating Web service development. However Web services fundamentally rely on XML and Schema, not on contemporary programming language type systems such as those of Java or .NET. Moreover, Web services are based on a messaging paradigm and hence bring forward the traditional problems of messaging systems including concurrency control and message correlation. It is easy to write simple synchronous Web services using traditional programming languages; however more realistic scenarios are surprisingly difficult to implement. To alleviate these issues we propose a programming language which directly supports Web service development. The language leverages XQuery for native XML processing, supports implicit message correlation and has high level join calculus-style concurrency control. We illustrate the features of the language through a motivating example

JXTA security in basic peer operations

Open Access Documen

Staircase Join: Teach a Relational DBMS to Watch its (Axis) Steps

Relational query processors derive much of their effectiveness from the awareness of specific table properties like sort order, size, or absence of duplicate tuples. This text applies (and adapts) this successful principle to database-supported XML and XPath processing: the relational system is made tree aware, i.e., tree properties like subtree size, intersection of paths, inclusion or disjointness of subtrees are made explicit. We propose a local change to the database kernel, the staircase join, which encapsulates the necessary tree knowledge needed to improve XPath performance. Staircase join operates on an XML encoding which makes this knowledge available at the cost of simple integer operations (e.g., +, <=). We finally report on quite promising experiments with a staircase join enhanced main-memory database kernel

Expressing OLAP operators with the TAX XML algebra

With the rise of XML as a standard for representing business data, XML data warehouses appear as suitable solutions for Web-based decision-support applications. In this context, it is necessary to allow OLAP analyses over XML data cubes (XOLAP). Thus, XQuery extensions are needed. To help define a formal framework and allow much-needed performance optimizations on analytical queries expressed in XQuery, having an algebra at one's disposal is desirable. However, XOLAP approaches and algebras from the literature still largely rely on the relational model and/or only feature a small number of OLAP operators. In opposition, we propose in this paper to express a broad set of OLAP operators with the TAX XML algebra.Comment: in 3rd International Workshop on Database Technologies for Handling XML Information on the Web (DataX-EDBT 08), Nantes : France (2008

AT-GIS: highly parallel spatial query processing with associative transducers

Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries

Security analysis of JXME-Proxyless version

JXME es la especificación de JXTA para dispositivos móviles con J2ME. Hay dos versiones diferentes de la aplicación JXME disponibles, cada una específica para un determinado conjunto de dispositivos, de acuerdo con sus capacidades. El principal valor de JXME es su simplicidad para crear peer-to-peer (P2P) en dispositivos limitados. Además de evaluar las funciones JXME, también es importante tener en cuenta el nivel de seguridad por defecto que se proporciona. Este artículo presenta un breve análisis de la situación actual de la seguridad en JXME, centrándose en la versión JXME-Proxyless, identifica las vulnerabilidades existentes y propone mejoras en este campo.JXME és l'especificació de JXTA per a dispositius mòbils amb J2ME. Hi ha dues versions diferents de l'aplicació JXME disponibles, cada una d'específica per a un determinat conjunt de dispositius, d'acord amb les seves capacitats. El principal valor de JXME és la seva simplicitat per crear peer-to-peer (P2P) en dispositius limitats. A més d'avaluar les funcions JXME, també és important tenir en compte el nivell de seguretat per defecte que es proporciona. Aquest article presenta un breu anàlisis de la situació actual de la seguretat en JXME, centrant-se en la versió JXME-Proxyless, identifica les vulnerabilitats existents i proposa millores en aquest camp.JXME is the JXTA specification for mobile devices using J2ME. Two different flavors of JXME implementation are available, each one specific for a particular set of devices, according to their capabilities. The main value of JXME is its simplicity to create peer-to-peer (P2P) applications in limited devices. In addition to assessing JXME functionalities, it is also important to realize the default security level provided. This paper presents a brief analysis of the current state of security in JXME, focusing on the JXME-Proxyless version, identifies existing vulnerabilities and proposes further improvements in this field

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”