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Development of the Unified Database Design Methodology for Big Data Applications - based on MongoDB -

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초록

The recent sudden increase of big data has characteristics such as continuous generation of data, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such big data due to the limited processing speed and the significant storage expansion cost. Current implemented solutions are mainly based on relational database that are no longer adapted to these data volume. NoSQL solutions allow us to consider new approaches for data warehousing, especially from the multidimensional data management point of view. In this paper, we develop and propose the integrated design methodology based on MongoDB for big data applications. The proposed methodology is more scalable than the existing methodology, so it is easy to handle big data.

Abstract

The recent sudden increase of big data has characteristics such as continuous generation of data, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such big data due to the limited processing speed and the significant storage expansion cost. Current implemented solutions are mainly based on relational database that are no longer adapted to these data volume. NoSQL solutions allow us to consider new approaches for data warehousing, especially from the multidimensional data management point of view. In this paper, we develop and propose the integrated design methodology based on MongoDB for big data applications. The proposed methodology is more scalable than the existing methodology, so it is easy to handle big data.

참고문헌 (12)

  1. Jacobs, A., "The pathologies of big data", Communications of the ACM, 52(8), pp.36~44 (2009)
  2. Stonebraker, M., "New Opportunities for New SQL", Communications of the ACM, 55(11), pp.10~11 (2012)
  3. Cuzzocrea, Alfredo, "Data warehousing and OLAP over big data: current challenges and future research directions", Proceedings of the sixteenth international workshop on Data warehousing and OLAP (2013), ACM
  4. Dehdouh, Khaled, "Columnar nosql star schema benchmark", International Conference on Model and Data Engineering (2014)
  5. Chevalier M, "Implementing Multidimensional Data Warehouses into NoSQL", 17th International Conference on Enterprise Information Systems, 1, pp.172~183 (2015)
  6. W.H.Inmon, "Building the Data Warehouse" (2002), John Wiley & Sons
  7. Conn, Samuel S., "OLTP and OLAP data integration: a review of feasible implementation methods and architectures for real time data analysis", SoutheastCon, 2005, pp.515~520 (2005), IEEE
  8. Chaudhuri, Surajit, "An overview of data warehousing and OLAP technology", ACM Sigmod record, 26(1), pp.65~74 (1997)[DOI]
  9. Michael Dirolf, "MongoDB The Definitive Guide" (2013), O'ReillyMedia
  10. Mamenko, J., "Introduction to data modeling and msaccess", Lecture Notes on Information Resources (2004)
  11. Hoberman, S., "DataModeling for MongoDB" (2014), Technics Publications
  12. Christopher Adamson, "Star Schema The Complete Reference" (2010), McGraw-Hill Osborne

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