Файл: Разработка прототипа базы знаний современного научного сообщества М. А. Григорьева.pdf

teristics of all stages of the experiment, and Virtuoso RDF -storage, where all e xtracted data are registered. DKB agents process and aggregate metadata fro m data manage ment and data processing systems, metadata interface, conference notes archives, workshops and meetings agendas, and publications. Additionally, this data is linking with the scientific topic documentation pages (such as Twiki pages, Google documents, etc) and informat ion e xtracted fro m full te xts of e xperiment supporting documentation. In this way, rather than require the physicists to annotate all meta
- informat ion in details, DKB agents will e xt ract, aggregate and integrate all necessary metadata automatica lly.
Keywords: data knowledge base, ontology, RDF-storage, Virtuoso, e xperiment life -cycle, data processing chain, scientific publication.
The work was supported by the Russian M inistry of Science and Education under contract No. 14.Z50.31.0024.
© 2016 Maria A. Grigorieva, Vasily A. Aulov, Marina V. Golosova, Maksim Y. Gubin, Alexei A. K limentov c- based chara
- storage, where data fro m many metadata sources are integrated and processed to obtain knowledge is functioning on the basis of HEP data analysis ontology. The architecture has two data storage layers: Hadoop and accompanying information. DKB
DKB) gives such possibility and provides fast access to relevant scientific
- based infrastructure (Data Knowledge Base
- initia l results presentation and final publication. A knowledge hypothesis following by processing chain description, data collection, must be preserved, starting fro m the initia l collection with new software releases or/and algorithms. That’s why all information about data analysis process s very important for the scientists to conduct studies under the same conditions or to process data data analysis, it’
tions, indexing and publication of the experimental results. Besides, to reproduce and to verify some previous a- is a very loose coupling between metadata describing data processing cycle, and metadata representing annot ntific commun ities and scientific results replicability and reproducibility. The actual issue for the majority of scie stages of an experiment life cycle a re accompanied with the auxilia ry metadata required for monitoring, control and exascale data volume. All
- g tools, peta e xperimental infrastructure, sophisticated data analysis and processin scale modern scientific e xpe riments are long lifetime , co mple x
-
The most common characteristics of large a le xei.klimentov@cern.ch e
maksim.gubin@cern.ch, d
, marina.golosova@cern.ch c
vasiliyaulov@g ma il.co m, b maria.g rigorieva@cern.ch, a ma il:
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5000, USA
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Brookhaven National Laboratory, Upton, NY 11973 3
National research To msk Po lytechnic University, 30, Len in Avenue, Tomsk, Russia
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National research Center “Kurchatov Institute”, 1, Akademika Kurchatova sq., Moscow, Russia
1
3,e
Klimentov
A.
A.
,
2,d
Gubin
, M. Y.
1,c
Golosova
, M. V.
1,b
Aulov
, V. A.
1,a
Grigorieva
M. A.
Collaborations
Data Knowledge Base Prototype for Modern Scientific
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