title: Efficient processing and classification of wave energy spectrum data with a distributed pipeline
metadata.name: dc.contributor.author
metadata.value: Ivan Gankevich
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Alexander Degtyarev
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2015-11-10T12:00:27Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2015-11-10T12:00:27Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2015
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/1758
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Processing of large amounts of data often consists of several steps, e.g. pre- and post-processing stages, which are executed sequentially with data written to disk after each step, however, when pre-processing stage for each task is different the more efficient way of processing data is to construct a pipeline which streams data from one stage to another. In a more general case some processing stages can be factored into several parallel subordinate stages thus forming a distributed pipeline where each stage can have multiple inputs and multiple outputs. Such processing pattern emerges in a problem of classification of wave energy spectra based on analytic approximations which can extract different wave systems and their parameters (e.g. wave system type, mean wave direction) from spectrum. Distributed pipeline approach achieves good performance compared to conventional “sequential-stage” processing.
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: Institute of Computer Science
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Efficient processing and classification of wave energy spectrum data with a distributed pipeline
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: gankevich2015spec(2).pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/1758/1/gankevich2015spec%282%29.pdf
resources.format: Adobe PDF
resources.description: Main article
resources.size: 581,81 kB