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fix xml error

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MaZderMind 2014-07-25 19:50:42 +02:00
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pydata14/schedule.xml
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@ -661,7 +661,7 @@
<license/>
<optout>false</optout>
</recording>
<room>B09/room>
<room>B09</room>
<language>en</language>
<abstract>Bloscpack [1] is a reference implementation and file-format for fast serialization of numerical data. It features lightweight, chunked and compressed storage, based on the extremely fast Blosc [2] metacodec and supports serialization of Numpy arrays out-of-the-box. Recently, Blosc -- being the metacodec that it is -- has received support for using the popular and widely used Snappy [3], LZ4 [4], and ZLib [5] codecs, and so, now Bloscpack supports serializing Numpy arrays easily with those codecs! In this talk I will present recent benchmarks of Bloscpack performance on a variety of artificial and real-world datasets with a special focus on the newly available codecs. In these benchmarks I will compare Bloscpack, both performance and usability wise, to alternatives such as Numpy's native offerings (NPZ and NPY), HDF5/PyTables [6], and if time permits, to novel bleeding edge solutions. Lastly I will argue that compressed and chunked storage format such as Bloscpack can be and somewhat already is a useful substrate on which to build more powerful applications such as online analytical processing engines and distributed computing frameworks. [1]: https://github.com/Blosc/bloscpack [2]: https://github.com/Blosc/c-blosc/ [3]: http://code.google.com/p/snappy/ [4]: http://code.google.com/p/lz4/ [5]: http://www.zlib.net/ [6]: http://www.pytables.org/moin</abstract>
<description>Bloscpack [1] is a reference implementation and file-format for fast serialization of numerical data. It features lightweight, chunked and compressed storage, based on the extremely fast Blosc [2] metacodec and supports serialization of Numpy arrays out-of-the-box. Recently, Blosc -- being the metacodec that it is -- has received support for using the popular and widely used Snappy [3], LZ4 [4], and ZLib [5] codecs, and so, now Bloscpack supports serializing Numpy arrays easily with those codecs! In this talk I will present recent benchmarks of Bloscpack performance on a variety of artificial and real-world datasets with a special focus on the newly available codecs. In these benchmarks I will compare Bloscpack, both performance and usability wise, to alternatives such as Numpy's native offerings (NPZ and NPY), HDF5/PyTables [6], and if time permits, to novel bleeding edge solutions. Lastly I will argue that compressed and chunked storage format such as Bloscpack can be and somewhat already is a useful substrate on which to build more powerful applications such as online analytical processing engines and distributed computing frameworks. [1]: https://github.com/Blosc/bloscpack [2]: https://github.com/Blosc/c-blosc/ [3]: http://code.google.com/p/snappy/ [4]: http://code.google.com/p/lz4/ [5]: http://www.zlib.net/ [6]: http://www.pytables.org/moin</description>