9,096 research outputs found
SFDL: MVC Applied to Workflow Design
Process management based on workflow systems is a growing trend in collaborative environments. One of the most notorious areas of improvement is that of user interfaces, especially since business process definition languages do not address efficiently the point of contact between workflow engines and human interactions. With that in focus, we propose the MVC pattern design to workflow systems. To accomplish this, we have designed a new dynamic view definition language called SFDL, oriented towards the easy interoperability with the different workflow definition languages, while maintaining enough flexibility to be represented in different formats and being adaptable to several environments. To validate our approach, we have carried out an implementation in a real banking scenario, which has provided continuous feedback and enabled us to refine the proposal. The work is fully based on widely accepted and used web standards (XML, YAML, JSON, Atom and REST). Some guidelines are given to facilitate the adoption of our solution
Many-Task Computing and Blue Waters
This report discusses many-task computing (MTC) generically and in the
context of the proposed Blue Waters systems, which is planned to be the largest
NSF-funded supercomputer when it begins production use in 2012. The aim of this
report is to inform the BW project about MTC, including understanding aspects
of MTC applications that can be used to characterize the domain and
understanding the implications of these aspects to middleware and policies.
Many MTC applications do not neatly fit the stereotypes of high-performance
computing (HPC) or high-throughput computing (HTC) applications. Like HTC
applications, by definition MTC applications are structured as graphs of
discrete tasks, with explicit input and output dependencies forming the graph
edges. However, MTC applications have significant features that distinguish
them from typical HTC applications. In particular, different engineering
constraints for hardware and software must be met in order to support these
applications. HTC applications have traditionally run on platforms such as
grids and clusters, through either workflow systems or parallel programming
systems. MTC applications, in contrast, will often demand a short time to
solution, may be communication intensive or data intensive, and may comprise
very short tasks. Therefore, hardware and software for MTC must be engineered
to support the additional communication and I/O and must minimize task dispatch
overheads. The hardware of large-scale HPC systems, with its high degree of
parallelism and support for intensive communication, is well suited for MTC
applications. However, HPC systems often lack a dynamic resource-provisioning
feature, are not ideal for task communication via the file system, and have an
I/O system that is not optimized for MTC-style applications. Hence, additional
software support is likely to be required to gain full benefit from the HPC
hardware
Towards Exascale Scientific Metadata Management
Advances in technology and computing hardware are enabling scientists from
all areas of science to produce massive amounts of data using large-scale
simulations or observational facilities. In this era of data deluge, effective
coordination between the data production and the analysis phases hinges on the
availability of metadata that describe the scientific datasets. Existing
workflow engines have been capturing a limited form of metadata to provide
provenance information about the identity and lineage of the data. However,
much of the data produced by simulations, experiments, and analyses still need
to be annotated manually in an ad hoc manner by domain scientists. Systematic
and transparent acquisition of rich metadata becomes a crucial prerequisite to
sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and
domain-agnostic metadata management infrastructure that can meet the demands of
extreme-scale science is notable by its absence.
To address this gap in scientific data management research and practice, we
present our vision for an integrated approach that (1) automatically captures
and manipulates information-rich metadata while the data is being produced or
analyzed and (2) stores metadata within each dataset to permeate
metadata-oblivious processes and to query metadata through established and
standardized data access interfaces. We motivate the need for the proposed
integrated approach using applications from plasma physics, climate modeling
and neuroscience, and then discuss research challenges and possible solutions
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Shortcomings of learning design approaches and a possible way out
Shifting away from traditional instructional design to younger research streams like personalized, workflow-based or collaborative e-learning, learning design (LD) has become an important issue in the field of technology-enhanced learning. Nevertheless, current LD approaches turn out to be rather unhandy or costly in teaching and research practice. In this paper, we discuss these shortcomings and propose an alternative solution approach which is based on a web application mashup, learner interactions, and a semantic layer for tool recommendations. As the evaluation of our first prototype is in progress, we can not highlight first experiences, but outline benefits and possible application scenarios in this position paper
Explorative search of distributed bio-data to answer complex biomedical questions
Background
The huge amount of biomedical-molecular data increasingly produced is providing scientists with potentially valuable information. Yet, such data quantity makes difficult to find and extract those data that are most reliable and most related to the biomedical questions to be answered, which are increasingly complex and often involve many different biomedical-molecular aspects. Such questions can be addressed only by comprehensively searching and exploring different types of data, which frequently are ordered and provided by different data sources. Search Computing has been proposed for the management and integration of ranked results from heterogeneous search services. Here, we present its novel application to the explorative search of distributed biomedical-molecular data and the integration of the search results to answer complex biomedical questions.
Results
A set of available bioinformatics search services has been modelled and registered in the Search Computing framework, and a Bioinformatics Search Computing application (Bio-SeCo) using such services has been created and made publicly available at http://www.bioinformatics.deib.polimi.it/bio-seco/seco/. It offers an integrated environment which eases search, exploration and ranking-aware combination of heterogeneous data provided by the available registered services, and supplies global results that can support answering complex multi-topic biomedical questions.
Conclusions
By using Bio-SeCo, scientists can explore the very large and very heterogeneous biomedical-molecular data available. They can easily make different explorative search attempts, inspect obtained results, select the most appropriate, expand or refine them and move forward and backward in the construction of a global complex biomedical query on multiple distributed sources that could eventually find the most relevant results. Thus, it provides an extremely useful automated support for exploratory integrated bio search, which is fundamental for Life Science data driven knowledge discovery
Reducing the Gap Between Business and Information Systems Through Complex Event Processing
According to the Object Management Group, a rule is a proposition that is a claim of obligation or of necessity. The concept of rule is usually employed in the context of business process to manage companies operations. While a workflow is an explicit specification of tasks' execution flow, business rules only impose restrictions on the tasks' execution. This provides a great deal of flexibility for the process execution, since the stakeholders are free to choose an execution flow which does not violate the rules. The execution of a task in a process can be seen as the occurrence of an event, which may enable/disable the execution of some other tasks in the process. Event-driven programming is a paradigm in which the program control-flow is determined by the occurrence of events. The capacity to handle processes that are unpredictably non-linear and dynamic makes the event-driven paradigm an effective solution for the implementation of business rules. However, the connection between the business rules and their implementation through event-driven programming has been made in an ad-hoc and unstructured manner. This paper proposes a methodology to tackle such a problem by systematically moving from business rules described in natural language toward a concrete implementation of a business process. We use complex event processing (CEP) to implement the process. CEP relies on the event driven paradigm for monitoring and processing events. The methodology allows for the active participation of business people at all stages of the refinement process. Throughout the paper, we show how our methodology was employed to implement the operations of the World Bank
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