Business intelligence and NoSQL databases

• Autorzy: Duda J.
• Języki publikacji: EN

Abstrakty

EN

NoSQL databases become more and more popular, not only in typical Internet applications. They allow to store large volumes of data (so called big data), while ensuring fast retrieving and fast appending. The main disadvantage of NoSQL databases is that they do not use relational model of data and usually do not offer any declarative query language similar to SQL. This raises the question how NoSQL databases can be used for OLAP processing and other Business Intelligence tasks. In the paper the author presents the most common types of NoSQL databases, describes MapReduce paradigm and discusses models of OLAP processing for such databases. Finally some preliminary results of aggregation performance in non-relational environment are presented.

Słowa kluczowe

EN

business intelligence; databases; NoSQL; big data; OLAP

PL

Business Intelligence; baza danych; NoSQL; OLAP; big data

• Wydawca: Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie
• Czasopismo: Information Systems in Management
• Rocznik: 2012
• Tom: Vol. 1, No. 1
• Strony: 25--37
• Opis fizyczny: Bibliogr. 17 poz., rys., tab.

Twórcy

autor

Duda J.

• Department of Applied Computer Science, Faculty of Management, AGH University of Science and Technology (AGH)

Bibliografia

• [1] NoSQL A Relational Database Management System, http://www.strozzi.it/cgibin/ CSA/tw7/I/en_US/NoSQL/Home Page, Retrieved 6 September 2012.
• [2] Lith A., Mattsson J. (2010) Investigating storage solutions for large data - A comparison of well performing and scalable data storage solutions for real time extraction and batch insertion of data, Department of Computer Science and Engineering, Chalmers University of Technology, Göteborg.
• [3] http://nosql-database.org, Retrieved 6 September 2012.
• [4] Abadi D. J., Turner M. J., Hammond R., Cotton P. (1979) A DBMS for large statistical databases, VLDB '79 Proceedings of the 5-th international conference on Very Large Data Bases.
• [5] Abadi D.J., Boncz P.A., Harizopoulos S. (2009) Column oriented Database Systems, PVLDB 2(2), 1664-1665.
• [6] Bajaj P., Dhindsa S.K. (2012) A Comparative Study of Database Systems, International Journal of Engineering and Innovative Technology, Volume 1, Issue 6
• [7] Dean J., Ghemawat S. (2008) MapReduce: Simplified Data Processing on Large Clusters, Communications of the ACM, Volume 51 Issue 1, 107-113.
• [8] Duarte de Souza R.G. (2010) MapReduce "Easy distributed computing", http://girlincomputerscience.blogspot.com/2010/12/mapreduce.html, Retrieved 8 September 2012.
• [9] Luhn H.P. (1958) A Business Intelligence System, IBM Journal 2 (4), 314-319.
• [10] Chee T., Chan L., Chuah M., Tan Ch., Wong S., Yeoh W. (2009) Business Intelligence Systems: State-Of-The-Art Review And Contemporary Applications, Symposium on Progress in Information & Communication Technology 2009.
• [11] Turban E., Sharda R., Delen D., King D. (2010) Business Intelligence, 2nd edition, Prentice Hall.
• [12] http://etl-tools.info/en/bi/datawarehouse_concepts.htm, Retrieved 10 September 2012.
• [13] Chaudhuri S., Dayal U. (1997) An Overview of Data Warehousing and OLAP Technology, SIGMOD Record 26(1), 65-74.
• [14] Bach Pedersen T., Jensen C. (2001) Multidimensional Database Technology, Distributed Systems Online (IEEE), 40-46.
• [15] Cubes OLAP avec Mondrian, http://www.osbi.fr, Retrieved 11 September 2012.
• [16] Cubes - OLAP Framework, http://packages.python.org/cubes, Retrieved 11-09-2012.
• [17] olap4cloud. User Guide, http://code.google.com/p/olap4cloud/wiki/UserGuide, Retrieved 12 September 2012.