Big Data Storage and Management: Challenges and Opportunities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F17%3A10363614" target="_blank" >RIV/00216208:11320/17:10363614 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Big Data Storage and Management: Challenges and Opportunities

Popis výsledku v původním jazyce

The paper is focused on today&apos;s very popular theme - Big Data. We describe and discuss its characteristics by eleven V&apos;s (Volume, Velocity, Variety, Veracity, etc.) and Big Data quality. These characteristics represent both data and process challenges. Then we continue with problems of Big Data storage and manage-ment. Principles of NoSQL databases are explained including their categorization. We also shortly describe Hadoop and MapReduce technologies as well as their inefficiency for some interactive queries and applications within the domain of large-scale graph processing and streaming data. NoSQL databases and Hadoop M/R are designed to take advantage of cloud computing architectures and allow massive computations to be run inexpensively and efficiently. The term of Big Data 1.0 was introduced for these technologies. We continue with some new ap-proaches called currently Big Data 2.0 processing systems. Particularly their four categories are introduced and discussed: General purpose Big Data Processing Systems, Big SQL Processing Systems, Big Graph Processing Systems, and Big Stream Processing Systems. Then, an attention is devoted to Big Analytics - the main application area for Big Data storage and processing. We argue that enter-prises with complex, heterogeneous environments no longer want to adopt a BI access point just for one data source (Hadoop). More heterogeneous software platforms are needed. Even Hadoop has become a multi-purpose engine for ad hoc analysis. Finally, we mention some problems with Big Data. We also remind that Big Data creates a new type of digital divide. Having access and knowledge of Big Data technologies gives companies and people a competitive edge in to-day&apos;s data driven world.

Název v anglickém jazyce

Big Data Storage and Management: Challenges and Opportunities

Popis výsledku anglicky

The paper is focused on today&apos;s very popular theme - Big Data. We describe and discuss its characteristics by eleven V&apos;s (Volume, Velocity, Variety, Veracity, etc.) and Big Data quality. These characteristics represent both data and process challenges. Then we continue with problems of Big Data storage and manage-ment. Principles of NoSQL databases are explained including their categorization. We also shortly describe Hadoop and MapReduce technologies as well as their inefficiency for some interactive queries and applications within the domain of large-scale graph processing and streaming data. NoSQL databases and Hadoop M/R are designed to take advantage of cloud computing architectures and allow massive computations to be run inexpensively and efficiently. The term of Big Data 1.0 was introduced for these technologies. We continue with some new ap-proaches called currently Big Data 2.0 processing systems. Particularly their four categories are introduced and discussed: General purpose Big Data Processing Systems, Big SQL Processing Systems, Big Graph Processing Systems, and Big Stream Processing Systems. Then, an attention is devoted to Big Analytics - the main application area for Big Data storage and processing. We argue that enter-prises with complex, heterogeneous environments no longer want to adopt a BI access point just for one data source (Hadoop). More heterogeneous software platforms are needed. Even Hadoop has become a multi-purpose engine for ad hoc analysis. Finally, we mention some problems with Big Data. We also remind that Big Data creates a new type of digital divide. Having access and knowledge of Big Data technologies gives companies and people a competitive edge in to-day&apos;s data driven world.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Environmental Software Systems, Computer Science for Environmental Protection

ISBN

978-3-319-89934-3

ISSN

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e-ISSN

neuvedeno

Počet stran výsledku

11

Strana od-do

28-38

Název nakladatele

SPRINGER

Místo vydání

Cham, Switzerland

Místo konání akce

Zadar

Datum konání akce

10. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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