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NoSQL databases as a data warehouse for decision support systems

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Języki publikacji
EN
Abstrakty
EN
Nowadays, to some extent decision support systems are forced to base their operation on large data warehouses whose analysis is difficult and time consuming. This is why where data are stored becomes vital. The use of an efficient and productive data warehouse for this purpose can significantly improve application/system operation. Currently one of the most common solutions used in Big Data storage and quick processing are non-relational databases NoSQL. They are a relatively new solution, however, their development is dynamic and their market share is increased on a daily basis, which means that it worth investigating what they offer.
Słowa kluczowe
EN
big data   NoSQL   ACID   CAP  
Twórcy
autor
  • Faculty of Informatics and Communication, University of Economics in Katowice
Bibliografia
  • 1. He Ch. (2015), Survey on NoSQL Database Technology, Journal of Applied Science and Engineering Innovation, Information Technology Service Center of Hexi University, Gansu Zhangye, China, vol.2, no 2, 50-54.
  • 2. Kirti, Maan P. (2015), Database for Unstructured, Semistructured data-NoSQL, International Journal of Advanced Research in Computer Engineering & Technology, Vol.4 Issue 2, 466-469.
  • 3. Ming Ch. Wu., Huang Y. F., Lee J. (2015), Comparisons Between MongoDB and MS-SQL Databases on the TWC Website, American Journal of Software Engineering and Applications, USA, 4(2), 35-41.
  • 4. Nataraja Sekhar G., Saritha S. S. J., Penchalaiah C. (2015), HBase Performance Testing On Multi-node Cluster Setup, International Journal Of Engineering And Computer Science, Mandsaur, India, Vol. 4, Issue 4, April, 11272-11278.
  • 5. Pańskowska M., Kurpanik J., NoSQL Problem Literature Reviews, Social Media & Creativity Support Systems pod red. Pańkowska M., Palonka J., UE Katowice, Katowice, 2015.
  • 6. Sadalage P., Fowler M. (2013), NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Pearson Education – Addison Wesley, Boston.
  • 7. Shibata T., Kurachi Y. (2015), Big Data Analysis Solution for Driving Innovation in Onsite Decision Making, Fujitsu Science Technology Journal, vol.51, no 2, 33-41.
  • 8. Tinkhede S. A., Deshpande S. P. (2015), Big Data - The Vast Growing Technology with its Challenges and Solutions, International Journal of Computer Science and Mobile Applications, vol.3, Issue 1, 33-38.
  • 9. [Online]. [access: 29.09.2015]. Available on the Internet: https://en.wikipedia.org/wiki/CAP_theorem.
  • 10. [Online]. [access: 29.09.2015]. Available on the Internet: https://pl.wikipedia.org/wiki/Big_data.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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