1,953 research outputs found
MonetDB/XQuery: a fast XQuery processor powered by a relational engine
Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met
Heterogeneous data source integration for smart grid ecosystems based on metadata mining
The arrival of new technologies related to smart grids and the resulting ecosystem of applications andmanagement systems pose many new problems. The databases of the traditional grid and the variousinitiatives related to new technologies have given rise to many different management systems with several formats and different architectures. A heterogeneous data source integration system is necessary toupdate these systems for the new smart grid reality. Additionally, it is necessary to take advantage of theinformation smart grids provide. In this paper, the authors propose a heterogeneous data source integration based on IEC standards and metadata mining. Additionally, an automatic data mining framework isapplied to model the integrated information.Ministerio de Economía y Competitividad TEC2013-40767-
Version Control in Online Software Repositories
Software version control repositories provide a uniform and stable interface to manage documents and their version histories. Unfortunately, Open Source systems, for example, CVS, Subversion, and GNU Arch are not well suited to highly collaborative environments and fail to track semantic changes in repositories. We introduce document provenance as our Description Logic framework to track the semantic changes in software repositories and draw interesting results about their historic behaviour using a rule-based inference engine. To support the use of this framework, we have developed our own online collaborative tool, leveraging the fluency of the modern WikiWikiWeb
Next-Generation EU DataGrid Data Management Services
We describe the architecture and initial implementation of the
next-generation of Grid Data Management Middleware in the EU DataGrid (EDG)
project.
The new architecture stems out of our experience and the users requirements
gathered during the two years of running our initial set of Grid Data
Management Services. All of our new services are based on the Web Service
technology paradigm, very much in line with the emerging Open Grid Services
Architecture (OGSA). We have modularized our components and invested a great
amount of effort towards a secure, extensible and robust service, starting from
the design but also using a streamlined build and testing framework.
Our service components are: Replica Location Service, Replica Metadata
Service, Replica Optimization Service, Replica Subscription and high-level
replica management. The service security infrastructure is fully GSI-enabled,
hence compatible with the existing Globus Toolkit 2-based services; moreover,
it allows for fine-grained authorization mechanisms that can be adjusted
depending on the service semantics.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla,Ca, USA, March 2003 8 pages, LaTeX, the file contains all
LaTeX sources - figures are in the directory "figures
Storage Solutions for Big Data Systems: A Qualitative Study and Comparison
Big data systems development is full of challenges in view of the variety of
application areas and domains that this technology promises to serve.
Typically, fundamental design decisions involved in big data systems design
include choosing appropriate storage and computing infrastructures. In this age
of heterogeneous systems that integrate different technologies for optimized
solution to a specific real world problem, big data system are not an exception
to any such rule. As far as the storage aspect of any big data system is
concerned, the primary facet in this regard is a storage infrastructure and
NoSQL seems to be the right technology that fulfills its requirements. However,
every big data application has variable data characteristics and thus, the
corresponding data fits into a different data model. This paper presents
feature and use case analysis and comparison of the four main data models
namely document oriented, key value, graph and wide column. Moreover, a feature
analysis of 80 NoSQL solutions has been provided, elaborating on the criteria
and points that a developer must consider while making a possible choice.
Typically, big data storage needs to communicate with the execution engine and
other processing and visualization technologies to create a comprehensive
solution. This brings forth second facet of big data storage, big data file
formats, into picture. The second half of the research paper compares the
advantages, shortcomings and possible use cases of available big data file
formats for Hadoop, which is the foundation for most big data computing
technologies. Decentralized storage and blockchain are seen as the next
generation of big data storage and its challenges and future prospects have
also been discussed
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