3,279 research outputs found
Community next steps for making globally unique identifiers work for biocollections data
Biodiversity data is being digitized and made available online at a rapidly increasing rate but current practices typically do not preserve linkages between these data, which impedes interoperation, provenance tracking, and assembly of larger datasets. For data associated with biocollections, the biodiversity community has long recognized that an essential part of establishing and preserving linkages is to apply globally unique identifiers at the point when data are generated in the field and to persist these identifiers downstream, but this is seldom implemented in practice. There has neither been coalescence towards one single identifier solution (as in some other domains), nor even a set of recommended best practices and standards to support multiple identifier schemes sharing consistent responses. In order to further progress towards a broader community consensus, a group of biocollections and informatics experts assembled in Stockholm in October 2014 to discuss community next steps to overcome current roadblocks. The workshop participants divided into four groups focusing on: identifier practice in current field biocollections; identifier application for legacy biocollections; identifiers as applied to biodiversity data records as they are published and made available in semantically marked-up publications; and cross-cutting identifier solutions that bridge across these domains. The main outcome was consensus on key issues, including recognition of differences between legacy and new biocollections processes, the need for identifier metadata profiles that can report information on identifier persistence missions, and the unambiguous indication of the type of object associated with the identifier. Current identifier characteristics are also summarized, and an overview of available schemes and practices is provided
Automated Model Synchronization: A Case Study on UML with Maude
Design specifications of software-intensive systems involve models that have been defined with different modelling languages for different purposes. Hence, a specification can be seen as the description of a system from multiple viewpoints, each providing domain-specific constructs for modelling the system in a more precise way. Such heterogeneity of models can jeopardize the consistency of the specification, because updates in one viewpoint may cause unpredictable design errors in other viewpoints, which can then be transferred to the implementation. OMG’s Meta-Object Facility enhances the automation of the model consistency management by providing a uniform format for different modelling languages. In this paper, we illustrate a technique, based on rewriting logic and on strategies for finding inconsistencies in MOF-based heterogeneous specifications and for resolving them in an automated way
Improving the scalability of parallel N-body applications with an event driven constraint based execution model
The scalability and efficiency of graph applications are significantly
constrained by conventional systems and their supporting programming models.
Technology trends like multicore, manycore, and heterogeneous system
architectures are introducing further challenges and possibilities for emerging
application domains such as graph applications. This paper explores the space
of effective parallel execution of ephemeral graphs that are dynamically
generated using the Barnes-Hut algorithm to exemplify dynamic workloads. The
workloads are expressed using the semantics of an Exascale computing execution
model called ParalleX. For comparison, results using conventional execution
model semantics are also presented. We find improved load balancing during
runtime and automatic parallelism discovery improving efficiency using the
advanced semantics for Exascale computing.Comment: 11 figure
GATE -- an Environment to Support Research and Development in Natural Language Engineering
We describe a software environment to support research and development in natural language (NL) engineering. This environment -- GATE (General Architecture for Text Engineering) -- aims to advance research in the area of machine processing of natural languages by providing a software infrastructure on top of which heterogeneous NL component modules may be evaluated and refined individually or may be combined into larger application systems. Thus, GATE aims to support both researchers and developers working on component technologies (e.g. parsing, tagging, morphological analysis) and those working on developing end-user applications (e.g. information extraction, text summarisation, document generation, machine translation, and second language learning). GATE will promote reuse of component technology, permit specialisation and collaboration in large-scale projects, and allow for the comparison and evaluation of alternative technologies. The first release of GATE is now available
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