2,650 research outputs found
Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1
The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization
An optical solution for the set splitting problem
We describe here an optical device, based on time-delays, for solving the set
splitting problem which is well-known NP-complete problem. The device has a
graph-like structure and the light is traversing it from a start node to a
destination node. All possible (potential) paths in the graph are generated and
at the destination we will check which one satisfies completely the problem's
constrains.Comment: 10 pages, 2 figure
Inter-organizational fault management: Functional and organizational core aspects of management architectures
Outsourcing -- successful, and sometimes painful -- has become one of the
hottest topics in IT service management discussions over the past decade. IT
services are outsourced to external service provider in order to reduce the
effort required for and overhead of delivering these services within the own
organization. More recently also IT services providers themselves started to
either outsource service parts or to deliver those services in a
non-hierarchical cooperation with other providers. Splitting a service into
several service parts is a non-trivial task as they have to be implemented,
operated, and maintained by different providers. One key aspect of such
inter-organizational cooperation is fault management, because it is crucial to
locate and solve problems, which reduce the quality of service, quickly and
reliably. In this article we present the results of a thorough use case based
requirements analysis for an architecture for inter-organizational fault
management (ioFMA). Furthermore, a concept of the organizational respective
functional model of the ioFMA is given.Comment: International Journal of Computer Networks & Communications (IJCNC
Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis
Traditional data centers are designed with a rigid architecture of
fit-for-purpose servers that provision resources beyond the average workload in
order to deal with occasional peaks of data. Heterogeneous data centers are
pushing towards more cost-efficient architectures with better resource
provisioning. In this paper we study the feasibility of using disaggregated
architectures for intensive data applications, in contrast to the monolithic
approach of server-oriented architectures. Particularly, we have tested a
proactive network analysis system in which the workload demands are highly
variable. In the context of the dReDBox disaggregated architecture, the results
show that the overhead caused by using remote memory resources is significant,
between 66\% and 80\%, but we have also observed that the memory usage is one
order of magnitude higher for the stress case with respect to average
workloads. Therefore, dimensioning memory for the worst case in conventional
systems will result in a notable waste of resources. Finally, we found that,
for the selected use case, parallelism is limited by memory. Therefore, using a
disaggregated architecture will allow for increased parallelism, which, at the
same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper
will be presented during the IEEE International Conference on High
Performance Computing and Communications in Bangkok, Thailand. 18 - 20
December, 2017. To be published in the conference proceeding
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
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