Open Access
Issue
ITM Web Conf.
Volume 8, 2016
International Conference on Big Data and its Applications (ICBDA 2016)
Article Number 01005
Number of page(s) 7
DOI https://doi.org/10.1051/itmconf/20160801005
Published online 22 November 2016
  1. V. Gaede. Multidimensional Access Methods /, Gaede V., Gunther O. // ACM Computer Surveys. 1998. Vol.30. No. 2. P. 170–231. [CrossRef] [Google Scholar]
  2. D. Greene. An Implementation and Performance Analysis of Spatial Data Access Methods/ Greene, D. // In Proceedings of the Fifth IEEE International Conference on Data Engineering. 1989. P. 606–615. [Google Scholar]
  3. A. M. Borodin, S. V. Porshnev, M. A. Sidorov. Application of Spatial Indices for the Processing of Analytical Retrievals and Aggregation of Multidimensional Data in IAS. Tomsk Polytechnical University Publishing. 2008 No 5, Pp. 64–86. [Google Scholar]
  4. M. Frialis. Data Management and Mining in Astrophysical Databases // PhD thesis, Univ.of Udine, Italy. 2005. [Google Scholar]
  5. K. T. Chang. Introduction to Geographical Information Systems /K. T. Chang //New York: McGraw Hill.-2008. P. 184. [Google Scholar]
  6. A. Andoni. Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions /Alexandr Andoni and Piotr Indyk //Communications of the ACM. Vol. 51. No.1. 2008. P. 117–122. [CrossRef] [Google Scholar]
  7. A. M. Borodin, S. V. Porshnev. Comparative Analysis of the Possibilities and the Rate of Processing of Multidimensional Data by the Business Analysis Software Based on the Main Memory Indexing Structures Scientific and Technical News of SPbGU. Series ”Computer Science, Telecommunication, Management” -2010. –No. 1. Pp. 99–102. [Google Scholar]
  8. V. K. Gulakov, A. O. Trubakov. Multidimensional Data Structures. 2010. Bryansk: BSU Publishing, P. 387. [Google Scholar]
  9. J. Hellerstein. Generalized Search Trees for Database Systems /J. Hellerstein, J. Naughton, A. Pfeffer // Proc. 21st Int’l Conf. on Very Large Data Bases. Zurich. September, 1995. P. 562–573. [Google Scholar]
  10. A. M. Borodin, S. V. Porshnev. Analytical Methods for Estimating the Efficiency of Spatial Indices Application in OLAP Systems. [Text]. Scientific and Technical News of SPbGU. Series ”Computer Science, Telecommunication, Management”-2011. Pp. 93–100. [Google Scholar]
  11. T. Johnson. Performance Measurements of Compressed Bitmap Indices / Johnson T.//-Proceedings of 25th International Conference on Very Large Data Bases. September 7-10, 1999. P. 278–289. [Google Scholar]
  12. M. Posumanskiy. Chronicle Number 9 “We Are Great Minds”. [Digital resource] //URL: http://web.archive.org/web/20040306084024/ http://www.mosha.com/XRONIKI/win-xronika9.html (access date 10/13/2013) [Google Scholar]
  13. A. M. Borodin, S. V. Porshnev. Algorithms for the Quick Access to Multidimensional Data in OLAP Systems. Saarbrucken: LAP Lambert Academic Publishing. 2012. P. 176 [Google Scholar]
  14. A. Rajaraman. Mining of Massive Datasets, Ch. 3. /A. Rajaraman, J. Ullman // Stanford University, California, 2010. P. 326. [Google Scholar]
  15. Yu Hua. BR-Tree: A Scalable Prototype for Supporting Multiple Queries of Multidimensional Data /Yu Hua, Bin Xiao, Jianping Wang // Computers, IEEE Transactions on. Vol. 58. Issue 12. 2009. P. 1585–1598. [CrossRef] [MathSciNet] [Google Scholar]
  16. B. H. Bloom. Space/time trade-offs in hash coding with allowable errors/ Burton H. Bloom // Communications of the ACMT. 13 (7). 1970. P. 422–426. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.