10,149 research outputs found
RDF Querying
Reactive Web systems, Web services, and Web-based publish/
subscribe systems communicate events as XML messages, and in
many cases require composite event detection: it is not sufficient to react
to single event messages, but events have to be considered in relation to
other events that are received over time.
Emphasizing language design and formal semantics, we describe the
rule-based query language XChangeEQ for detecting composite events.
XChangeEQ is designed to completely cover and integrate the four complementary
querying dimensions: event data, event composition, temporal
relationships, and event accumulation. Semantics are provided as
model and fixpoint theories; while this is an established approach for rule
languages, it has not been applied for event queries before
Data Model and Query Constructs for Versatile Web Query Languages
As the Semantic Web is gaining momentum, the need for
truly versatile query languages becomes increasingly apparent. A Web
query language is called versatile if it can access in the same query program
data in different formats (e.g. XML and RDF). Most query languages
are not versatile: they have not been specifically designed to cope
with both worlds, providing a uniform language and common constructs
to query and transform data in various formats. Moreover, most of them
do not provide a flexible data model that is powerful enough to naturally
convey both Semantic Web data formats (especially RDF and
Topic Maps) and XML. This article highlights challenges related to the
data model and language constructs for querying both standard Web
and Semantic Web data with an emphasis on facilitating sophisticated
reasoning. It is shown that Xcerpt’s data model and querying constructs
are particularly well-suited for the Semantic Web, but that some adjustments
of the Xcerpt syntax allow for even more effective and natural
querying of RDF and Topic Maps
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”
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
IVOA Recommendation: Universal Worker Service Pattern Version 1.0
The Universal Worker Service (UWS) pattern defines how to manage asynchronous
execution of jobs on a service. Any application of the pattern defines a family
of related services with a common service contract. Possible uses of the
pattern are also described
Stocator: A High Performance Object Store Connector for Spark
We present Stocator, a high performance object store connector for Apache
Spark, that takes advantage of object store semantics. Previous connectors have
assumed file system semantics, in particular, achieving fault tolerance and
allowing speculative execution by creating temporary files to avoid
interference between worker threads executing the same task and then renaming
these files. Rename is not a native object store operation; not only is it not
atomic, but it is implemented using a costly copy operation and a delete.
Instead our connector leverages the inherent atomicity of object creation, and
by avoiding the rename paradigm it greatly decreases the number of operations
on the object store as well as enabling a much simpler approach to dealing with
the eventually consistent semantics typical of object stores. We have
implemented Stocator and shared it in open source. Performance testing shows
that it is as much as 18 times faster for write intensive workloads and
performs as much as 30 times fewer operations on the object store than the
legacy Hadoop connectors, reducing costs both for the client and the object
storage service provider
- …