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| Description
| - The concept of Big Data has progressively become the next evolutionary phase in batch processing, storing, manipulation and relations visualization in vast number of records. Computational power required for these operations is usually abundant in organizations dealing with Big Data due to overprovisioned IT infrastructure or migration to cloud, lack of experts with experience in statistics, data mining, machine learning and databases pose a challenge, however. As investments from medium- and large-sized companies are expected to grow substantially within the area in future, demand for data scientists will most certainly follow a similar trend. Tertiary education system should reflect these changes by introducing curricula encompassing not only traditional, but also new and multidisciplinary subjects such as data visualization, temporal and spatial data analysis, distributed processing as well as case studies based on publicly available data sets. Unfortunately, so far only a handful of universities initiated such incentives and restructuring, leaving Big Data without institutionalized framework where students and professionals alike may prepare for the positions of commercial, scientific and general-purpose data scientists. As organizations dealing with Big Data may have specific requirements, the opportunity to engage in academia – practice discussion and cooperation certainly exist. The article aims to present fundamental prerequisites for data scientists as well as to provide overview of massive databases management tools along with differing approaches towards Big Data compared to classical databases architectures. By means of extensive secondary sources overview, a comprehensive profile of currently used technologies and concepts is presented which may constitute basis for a university curriculum. Also presented are viable research venues related to Big Data and data science.
- The concept of Big Data has progressively become the next evolutionary phase in batch processing, storing, manipulation and relations visualization in vast number of records. Computational power required for these operations is usually abundant in organizations dealing with Big Data due to overprovisioned IT infrastructure or migration to cloud, lack of experts with experience in statistics, data mining, machine learning and databases pose a challenge, however. As investments from medium- and large-sized companies are expected to grow substantially within the area in future, demand for data scientists will most certainly follow a similar trend. Tertiary education system should reflect these changes by introducing curricula encompassing not only traditional, but also new and multidisciplinary subjects such as data visualization, temporal and spatial data analysis, distributed processing as well as case studies based on publicly available data sets. Unfortunately, so far only a handful of universities initiated such incentives and restructuring, leaving Big Data without institutionalized framework where students and professionals alike may prepare for the positions of commercial, scientific and general-purpose data scientists. As organizations dealing with Big Data may have specific requirements, the opportunity to engage in academia – practice discussion and cooperation certainly exist. The article aims to present fundamental prerequisites for data scientists as well as to provide overview of massive databases management tools along with differing approaches towards Big Data compared to classical databases architectures. By means of extensive secondary sources overview, a comprehensive profile of currently used technologies and concepts is presented which may constitute basis for a university curriculum. Also presented are viable research venues related to Big Data and data science. (en)
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| Title
| - Science of Big Data: Background and Requirements
- Science of Big Data: Background and Requirements (en)
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| skos:prefLabel
| - Science of Big Data: Background and Requirements
- Science of Big Data: Background and Requirements (en)
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| skos:notation
| - RIV/70883521:28120/12:43868678!RIV13-MSM-28120___
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| http://linked.open...avai/predkladatel
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/70883521:28120/12:43868678
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Big, data, database, knowledge, discovery, mining, processing, curriculum, BigTable, Dynamo, Hadoop, MapReduce, distributed, cloud (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...v/mistoKonaniAkce
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...i/riv/nazevZdroje
| - Advances in Economics, Risk Management, Political and Law Science
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
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| http://linked.open...vavai/riv/typAkce
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| http://linked.open.../riv/zahajeniAkce
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| issn
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| number of pages
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| http://purl.org/ne...btex#hasPublisher
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| https://schema.org/isbn
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| http://localhost/t...ganizacniJednotka
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| is http://linked.open...avai/riv/vysledek
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